Determination of strategy modes for autonomous vehicle operations

ABSTRACT

Systems and methods for determining strategy modes for autonomous vehicles are described. An autonomous vehicle may detect aspects of other vehicles and aspects of the environment using one or more sensors. The autonomous vehicle may then determine strategy modes of the other vehicles, and select a strategy mode for its own operation based on the determined strategy modes and an operational goal for the autonomous vehicle. The strategy modes may include an uncoupled strategy mode, a permissive strategy mode, an assistive strategy mode, and a preventative strategy mode. The autonomous vehicle may further determine elements in the environment and topological constraints associated with the environment, and select the strategy mode for its own operation based thereon.

BACKGROUND

Autonomous vehicles, including ground-based vehicles and aerialvehicles, are in various stages of development. Much of the currentdevelopment work focuses on the internal operations of any oneautonomous vehicle and the level of autonomy associated with eachindividual autonomous vehicle. However, autonomous vehicles, such asautonomous cars, may be operated in environments together with other,non-autonomous vehicles, such as conventional cars operated by humans,and safety is generally regarded as the highest priority when autonomousvehicles are operated in environments where humans may be present.Accordingly, there is a need for systems and methods that enable safe,resolvable, and efficient operation of autonomous vehicles andnon-autonomous vehicles in a variety of environments.

BRIEF DESCRIPTION OF THE DRAWINGS

The detailed description is described with reference to the accompanyingfigures. In the figures, the left-most digit(s) of a reference numberidentifies the figure in which the reference number first appears. Theuse of the same reference numbers in different figures indicates similaror identical components or features.

FIG. 1 is a table of strategy modes for autonomous vehicle operations,according to an implementation.

FIG. 2A is a schematic diagram of a first strategy mode for autonomousvehicle operations in a first example environment, according to animplementation.

FIG. 2B is a schematic diagram of a second strategy mode for autonomousvehicle operations in the first example environment, according to animplementation.

FIG. 2C is a schematic diagram of a third strategy mode for autonomousvehicle operations in the first example environment, according to animplementation.

FIG. 2D is a schematic diagram of a fourth strategy mode for autonomousvehicle operations in the first example environment, according to animplementation.

FIG. 3A is a schematic diagram of a first strategy mode for autonomousvehicle operations in a second example environment, according to animplementation.

FIG. 3B is a schematic diagram of a second strategy mode for autonomousvehicle operations in the second example environment, according to animplementation.

FIG. 3C is a schematic diagram of a third strategy mode for autonomousvehicle operations in the second example environment, according to animplementation.

FIG. 3D is a schematic diagram of a fourth strategy mode for autonomousvehicle operations in the second example environment, according to animplementation.

FIG. 4A is a schematic diagram of strategy modes for autonomous vehicleoperations in a third example environment, according to animplementation.

FIG. 4B is a schematic diagram of strategy modes for autonomous vehicleoperations in a fourth example environment, according to animplementation.

FIG. 4C is a schematic diagram of strategy modes for autonomous vehicleoperations in a fifth example environment, according to animplementation.

FIG. 5 is a flow diagram illustrating an example strategy mode selectionprocess for autonomous vehicle operations, according to animplementation.

FIG. 6 is a schematic diagram of coordinated optimization of strategymodes for autonomous vehicle operations in a first example environment,according to an implementation.

FIG. 7 is a schematic diagram of coordinated optimization of strategymodes for autonomous vehicle operations in a second example environment,according to an implementation.

FIG. 8 is a flow diagram illustrating an example coordinatedoptimization process for autonomous vehicle operations, according to animplementation.

FIG. 9 is a block diagram of an example autonomous vehicle, according toan implementation.

FIG. 10 is a flow diagram illustrating an example strategy modebroadcast process for autonomous vehicle operations, according to animplementation.

FIG. 11 is a flow diagram illustrating an example strategy modedetermination process for autonomous vehicle operations, according to animplementation.

FIG. 12 is a block diagram illustrating various components of anautonomous vehicle control system for selection, broadcast, anddetermination of strategy modes for autonomous vehicle operations,according to an implementation.

FIG. 13 is a block diagram illustrating various components of anautonomous vehicle management system for coordinated optimization ofstrategy modes for autonomous vehicle operations, according to animplementation.

While implementations are described herein by way of example, thoseskilled in the art will recognize that the implementations are notlimited to the examples or drawings described. It should be understoodthat the drawings and detailed description thereto are not intended tolimit implementations to the particular form disclosed but, on thecontrary, the intention is to cover all modifications, equivalents andalternatives falling within the spirit and scope as defined by theappended claims. The headings used herein are for organizationalpurposes only and are not meant to be used to limit the scope of thedescription or the claims. As used throughout this application, the word“may” is used in a permissive sense (i.e., meaning having the potentialto), rather than the mandatory sense (i.e., meaning must). Similarly,the words “include,” “including,” and “includes” mean including, but notlimited to.

DETAILED DESCRIPTION

Various strategy modes for autonomous vehicle operations are describedherein. The strategy modes may generally relate to the interoperabilityof autonomous vehicles with other types of vehicles in variousenvironments. The other types of vehicles may include other autonomousvehicles, each of which may have different levels of autonomy, othernon-autonomous vehicles, such as vehicles operated by humans, othertypes of autonomous or non-autonomous vehicles, such as automobiles,other ground-based vehicles, air-based vehicles, water-based vehicles,space-based vehicles, automated guided vehicles, or other roboticvehicles or systems, or any other types of vehicles or systems that maybe operated in environments together with autonomous vehicles.

The strategy modes for autonomous vehicle operations may include fourgeneral strategy modes, including an uncoupled strategy mode, apermissive strategy mode, an assistive strategy mode, and a preventativestrategy mode.

When operating in an uncoupled strategy mode, an autonomous vehicle mayselect actions that may not affect or be affected by a strategy modeand/or action of a second vehicle. For example, if the autonomousvehicle and the second vehicle are facing each other at an intersectionhaving a four-way stop sign, and if the strategy mode and/or action ofthe second vehicle is unknown or cannot be determined, the autonomousvehicle may choose an uncoupled strategy mode that may not affect or beaffected by the second vehicle. Corresponding uncoupled actions of theautonomous vehicle may include waiting for the second vehicle to take anaction, reversing in the direction from which the autonomous vehiclearrived at the intersection, or performing a U-turn and returning backto the direction from which the autonomous vehicle arrived at theintersection.

When operating in a permissive, or coupled, strategy mode, an autonomousvehicle may continue actions that permit or allow a strategy mode and/oraction of a second vehicle to be successfully performed withoutrequiring a change to current actions of the autonomous vehicle. Forexample, if the autonomous vehicle and the second vehicle are facingeach other at an intersection having a four-way stop sign, and theautonomous vehicle detects the strategy mode and/or action of the secondvehicle as performing a left turn operation, the autonomous vehicle maychoose a permissive strategy mode to continue current actions withoutchange. Corresponding permissive actions of the autonomous vehicle mayinclude continuing to wait until the second vehicle completes its leftturn, continuing a forward motion at a slower speed so that the secondvehicle completes its left turn before the autonomous vehicle enters theintersection, or continuing a forward motion at a higher speed throughthe intersection before the second vehicle begins its left turn.

When operating in an assistive strategy mode, an autonomous vehicle mayselect actions that facilitate a strategy mode and/or action of a secondvehicle in being successfully performed by causing a change to currentactions of the autonomous vehicle. For example, if the autonomousvehicle and the second vehicle are facing each other at an intersectionhaving a four-way stop sign, and the autonomous vehicle detects thestrategy mode and/or action of the second vehicle as performing a leftturn operation, the autonomous vehicle may choose an assistive strategymode to alter current actions of the autonomous vehicle. Correspondingassistive actions of the autonomous vehicle may include stopping andwaiting until the second vehicle completes its left turn, deceleratinguntil the second vehicle completes its left turn, or accelerating pastthe second vehicle before the second vehicle begins its left turn.

When operating in a preventative strategy mode, an autonomous vehiclemay select actions that prevent a strategy mode and/or action of asecond vehicle from being successfully performed by causing a change tocurrent actions of the autonomous vehicle. For example, if theautonomous vehicle and the second vehicle are facing each other at anintersection having a four-way stop sign, and the autonomous vehicledetects the strategy mode and/or action of the second vehicle asperforming a left turn operation which may be an unsafe or otherwiseundesirable operation (e.g., due to an accident, construction, trafficcongestion, or other incident in that direction), the autonomous vehiclemay choose a preventative strategy mode to alter current actions of theautonomous vehicle. Corresponding preventative actions of the autonomousvehicle may include blocking the roadway that leads to the accident,construction, traffic congestion, or other incident, or blocking thesecond vehicle to prevent its left turn.

An autonomous vehicle may select a particular strategy mode based on avariety of information related to the autonomous vehicle itself, othervehicles, and/or the environment. In one embodiment, the autonomousvehicle may not detect any information about other vehicles and/or theenvironment and may select a strategy mode based on its intendedoperation. In some embodiments, the autonomous vehicle may detectinformation about other vehicles and their strategy modes and/or actionsand may select a strategy mode based on such information. In otherembodiments, the autonomous vehicle may detect information about theenvironment, as well as other vehicles and their strategy modes and/oractions, and may select a strategy mode based on such information.

In addition, an autonomous vehicle may select a particular strategy modebased on one or more operational goals of the autonomous vehicle. Forexample, if an autonomous vehicle prioritizes on-time arrival at aparticular destination by a particular time, one or more strategy modesand/or corresponding actions may be selected by the autonomous vehicleto facilitate its arrival at the destination by the designated time.Further, if an autonomous vehicle prioritizes safe operation in allsituations, one or more strategy modes and/or corresponding actions maybe selected by the autonomous vehicle to maximize safety at all times.In this manner, an autonomous vehicle may prioritize any one or more ofvarious operational goals at various times during its operation, andselect strategy modes and/or actions that allow the autonomous vehicleto achieve its operational goals.

Each of the strategy modes may include a corresponding set of availableactions that may be taken by the autonomous vehicle. The set ofavailable actions may vary based on the detected information related tothe autonomous vehicle itself, other vehicles, and/or the environment.For example, if an autonomous vehicle has a minimum turning radius, theautonomous vehicle may not be able to select a U-turn action inenvironments in which the minimum turning radius is too large to executea successful U-turn action. In addition, if a second vehicle is blockingone possible path of the autonomous vehicle, the autonomous vehicle maynot be able to select an action to proceed along that path until thesecond vehicle is no longer blocking that path. Further, if a trafficsignal indicates a red light, or if a pedestrian is crossing in front ofthe autonomous vehicle, an autonomous vehicle may not be able to selectan action to proceed forward until the traffic signal indicates a greenlight, or until the pedestrian has completely crossed in front of theautonomous vehicle.

An autonomous vehicle management system may receive information from aplurality of vehicles, including autonomous vehicles and non-autonomousvehicles. The information may include strategy modes of the vehicles,actions of the vehicles, aspects of various environments, andtopological constraints of various environments. The autonomous vehiclemanagement system may process the received information and performoptimizations of autonomous vehicle operations in accordance with one ormore operational goals of the system as a whole. The autonomous vehiclemanagement system may alter or modify the operations of one or moreautonomous vehicles to meet one or more operational goals. For example,to minimize the time to arrival of an emergency vehicle to the scene ofan accident, the autonomous vehicle management system may modifyoperations or routing of other autonomous vehicles to facilitate accessto the scene by the emergency vehicle. Further, to evenly distribute theload of vehicles throughout a road network, the autonomous vehiclemanagement system may modify operations or routing of one or moreautonomous vehicles to facilitate substantially evenly distributed usageof the road network by all vehicles. Accordingly, an autonomous vehiclemanagement system may optimize for any one or more of variousoperational goals at various times.

Each autonomous vehicle may broadcast or share its strategy mode, andother information, with other vehicles. To accomplish this, eachautonomous vehicle may include a broadcasting or notification devicethat broadcasts or transmits its strategy mode to other vehicles. Thebroadcasting or notification device may comprise a visual notificationdevice, such as one or more lights or a visual identifier, an audionotification device, such as one or more speakers that emit sounds, or asignal notification device, such as one or more transponders orelectromagnetic or radiofrequency transmitters that emit electromagneticor radiofrequency signals, respectively.

In addition, each autonomous vehicle may also detect strategy modes, andother information, of other vehicles. To accomplish this, eachautonomous vehicle may include a sensing device that detects or receivesstrategy modes from other vehicles. The sensing device may comprise avisual sensing device, such as one or more imaging devices, an audiosensing device, such as one or more microphones, or a signaldetection/receiving device, such as one or more electromagnetic signalreceivers or radiofrequency identification readers. Moreover, eachvehicle may also include other sensors to detect aspects of theenvironment, and/or to detect or monitor internal operations,capabilities, and functions of the vehicle itself.

Furthermore, the broadcasting/notification devices and/or the sensingdevices may be separate or combined units or systems that may beinstalled, used, or retrofitted in other vehicles, e.g., non-autonomousvehicles operated by humans, or various types of autonomous vehicles. Bythe incorporation of such systems into non-autonomous vehicles, driversof non-autonomous vehicles may be able to selectively broadcast theirstrategy modes and/or actions to other vehicles, drivers ofnon-autonomous vehicles may also be alerted to the strategy modes and/oractions of other vehicles, and/or autonomous vehicles may be able todetect strategy modes and/or actions of non-autonomous vehicles.

FIG. 1 is a table 100 of strategy modes for autonomous vehicleoperations, according to an implementation. In the first column, thetable 100 identifies the four strategy modes for autonomous vehicleoperations, including an uncoupled strategy mode 102, a permissivestrategy mode 104, an assistive strategy mode 106, and a preventativestrategy mode 108. In the second column, the table 100 identifies adetected action of a second or other vehicle. The second or othervehicle may be an autonomous vehicle, a non-autonomous vehicle, adifferent type of autonomous vehicle, or any other autonomous ornon-autonomous vehicle or system.

In the third column, the table 100 identifies a corresponding type ofaction that the autonomous vehicle may perform based on the selectedstrategy mode 102, 104, 106, 108. For example, when operating in theuncoupled strategy mode 102, an autonomous vehicle may select an actionthat is uncoupled from any action by the other vehicle. The uncoupledaction of the autonomous vehicle may not affect or be affected by anyaction by the other vehicle. When operating in the permissive strategymode 104, an autonomous vehicle may determine to continue a currentaction without change to permit or allow an action by the other vehicle.The permissive action, e.g., the continued current action withoutchange, may be determined to be consistent or compatible with the actionby the other vehicle. When operating in the assistive strategy mode 106,an autonomous vehicle may select an action that will assist an action bythe other vehicle. The assistive action may be required by theautonomous vehicle in order to facilitate the successful performance ofthe action by the other vehicle. When operating in the preventativestrategy mode 108, an autonomous vehicle may select an action that willprevent an action by the other vehicle. The preventative action may bedetermined by the autonomous vehicle to be desirable or necessary toprevent the action by the other vehicle.

An autonomous vehicle may select from among the four strategy modesbased on any amount of detected or received information. In oneembodiment, the autonomous vehicle may select a strategy mode withoutdetection, receipt, processing, or analysis of any information aboutother vehicles or the environment. In some embodiments, the autonomousvehicle may select a strategy mode based on detected or receivedinformation about other vehicles and their strategy modes and/oractions. In other embodiments, the autonomous vehicle may select astrategy mode based on detected or received information about theenvironment, as well as other vehicles and their strategy modes and/oractions. In still further embodiments, the autonomous vehicle may selecta strategy mode based on detected or determined topological constraintsassociated with the environment.

For example, during the performance of any action and/or during atransition between two or more actions associated with a selectedstrategy mode, the autonomous vehicle may continually detect or receiveinformation about other vehicles and/or the environment. During suchtransition stages, the autonomous vehicle may continue to update itsselection of a strategy mode based on the detected or receivedinformation. For example, other vehicles may alter their strategy modesand/or actions, such that the autonomous vehicle may then update itsstrategy mode and/or actions based on such detected changes. Inaddition, detected or determined topological constraints may be utilizedby the autonomous vehicle to select from among available actionsassociated with the selected strategy mode. For example, if anautonomous vehicle has a particular threshold topological number at orabove which it seeks to operate, the autonomous vehicle may select anaction from the available set of actions based on the detected ordetermined topological constraints, as well as predicted topologicalconstraints associated with selecting and performing an action, in orderto maintain its operation within the environment at or above thethreshold topological number during the performance of the action and/orduring transitions between two or more actions. Moreover, suchtopological constraints may change over time, e.g., from an initialstate before performing an action, during a transition state whileperforming the action, and upon reaching a final state after completingthe action, as the autonomous vehicle moves within the environmentand/or as other vehicles move or alter their actions within theenvironment.

FIGS. 2A, 2B, 2C, and 2D are schematic diagrams of the four strategymodes 102, 104, 106, 108 for autonomous vehicle operations in a firstexample environment, according to an implementation. The first exampleenvironment includes a roadway or highway having multiple lanes intendedfor travel in the same direction by autonomous and non-autonomousvehicles.

FIG. 2A includes an autonomous vehicle 205 with an intended travel 207and a second vehicle 210 with an intended travel 212, in which theautonomous vehicle 205 may select an uncoupled strategy mode 102. Thesecond vehicle 210 may be an autonomous or non-autonomous vehicle. Inaddition, FIG. 2A includes a barrier or divider 215 between two sets oflanes of the roadway. As shown in FIG. 2A, the autonomous vehicle 205may detect the second vehicle 210 and its strategy mode and/or actions.For example, the autonomous vehicle 205 may detect the intended travel212 of the second vehicle 210 as it begins to change lanes. However,because of the presence of the divider 215 between the set of lanes onwhich the second vehicle 210 is traveling and the set of lanes on whichthe autonomous vehicle 205 is traveling, the autonomous vehicle 205 mayselect an uncoupled strategy mode and any corresponding action that doesnot affect or is not affected by the second vehicle 210.

Referring to FIG. 2A, the set of available actions for the uncoupledstrategy mode of the autonomous vehicle 205 may include accelerating,maintaining a current speed, decelerating, stopping, changing lanes,and/or combinations thereof because of the uncoupled status of theoperations of the second vehicle 210 and the operations of theautonomous vehicle 205.

FIG. 2B includes an autonomous vehicle 205 with an intended travel 207and a second vehicle 210 with an intended travel 212, in which theautonomous vehicle 205 may select a permissive strategy mode 104. Thesecond vehicle 210 may be an autonomous or non-autonomous vehicletraveling at a higher speed than the autonomous vehicle 205. As shown inFIG. 2B, the autonomous vehicle 205 may detect the second vehicle 210and its strategy mode and/or actions. For example, the autonomousvehicle 205 may detect the intended travel 212 of the second vehicle 210as it passes the autonomous vehicle 205 and begins to change lanes.Because the autonomous vehicle 205 is traveling at a slower speed thanthat of the second vehicle 210, the autonomous vehicle 205 may select apermissive strategy mode and continue its current actions so as topermit the operations by the second vehicle 210.

Referring to FIG. 2B, the set of available actions for the permissivestrategy mode of the autonomous vehicle 205 may include maintaining acurrent speed, direction, and/or lane so as to permit or allow theoperations of the second vehicle 210 without change to the operations ofthe autonomous vehicle 205.

FIG. 2C includes an autonomous vehicle 205 with an intended travel 207and a second vehicle 210 with an intended travel 212, in which theautonomous vehicle 205 may select an assistive strategy mode 106. Thesecond vehicle 210 may be an autonomous or non-autonomous vehicletraveling at a similar speed as the autonomous vehicle 205. As shown inFIG. 2C, the autonomous vehicle 205 may detect the second vehicle 210and its strategy mode and/or actions. For example, the autonomousvehicle 205 may detect the intended travel 212 of the second vehicle 210as it travels next to the autonomous vehicle 205 and begins to changelanes. Because the autonomous vehicle 205 is traveling at a similarspeed as the second vehicle 210 and may recognize a conflict between theoperations of the second vehicle 210 and its own operations, theautonomous vehicle 205 may select an assistive strategy mode tofacilitate the operations by the second vehicle 210.

Referring to FIG. 2C, the set of available actions for the assistivestrategy mode of the autonomous vehicle 205 may include accelerating toallow the second vehicle to change lanes, decelerating to allow thesecond vehicle to change lanes, stopping, flashing lights, emittingaudible noises, moving over to the shoulder of the roadway, and/orcombinations thereof so as to facilitate the operations of the secondvehicle 210 by changing the operations of the autonomous vehicle 205.

FIG. 2D includes an autonomous vehicle 205 with an intended travel 207,a second vehicle 210 with an intended travel 212, and a third vehicle220 with an intended travel 222, in which the autonomous vehicle 205 mayselect a preventative strategy mode 108. The second vehicle 210 may bean autonomous or non-autonomous vehicle traveling at a higher speed thanthe autonomous vehicle 205, and the third vehicle 220 may also be anautonomous or non-autonomous vehicle traveling at a higher speed thanthe autonomous vehicle 205. As shown in FIG. 2D, the autonomous vehicle205 may detect the second vehicle 210 and its strategy mode and/oractions, and the autonomous vehicle 205 may also detect the thirdvehicle 220 and its strategy mode and/or actions. For example, theautonomous vehicle 205 may detect the intended travel 212 of the secondvehicle 210 as it travels next to the autonomous vehicle 205 and beginsto change lanes, and the autonomous vehicle 205 may also detect theintended travel 222 of the third vehicle 220 as it travels next to theautonomous vehicle 205. Because the autonomous vehicle 205 may recognizea conflict between the operations of the second vehicle 210 and thethird vehicle 220, the autonomous vehicle 205 may select a preventativestrategy mode to prevent the operations by the second vehicle 210 and/orthe third vehicle 220.

Referring to FIG. 2D, the set of available actions for the preventativestrategy mode of the autonomous vehicle 205 may include accelerating toprevent the second vehicle from changing lanes, changing lanes toprevent the third vehicle from passing, flashing lights, emittingaudible noises, and/or combinations thereof so as to prevent theoperations of the second vehicle 210 and/or the third vehicle 220 bychanging the operations of the autonomous vehicle 205.

FIGS. 3A, 3B, 3C, and 3D are schematic diagrams of the four strategymodes 102, 104, 106, 108 for autonomous vehicle operations in a secondexample environment, according to an implementation. The second exampleenvironment includes a four-way intersection in which autonomous andnon-autonomous vehicles may approach from or depart towards any of thefour directions of the intersection.

FIG. 3A includes an autonomous vehicle 305 with an intended travel 307and a second vehicle 310 with an intended travel 312, in which theautonomous vehicle 305 may select an uncoupled strategy mode 102. Thesecond vehicle 310 may be an autonomous or non-autonomous vehicle. Asshown in FIG. 3A, the autonomous vehicle 305 may not be able to detectthe strategy mode and/or actions of the second vehicle 310. For example,the second vehicle 310 may be stopped at the four-way intersection andmay not be broadcasting or transmitting its strategy mode or any otherinformation. As a result, the autonomous vehicle 305 may select anuncoupled strategy mode and any corresponding action that does notaffect or is not affected by the second vehicle 310. One example actionthat the autonomous vehicle 305 may take is shown as the intended travel307, in which the autonomous vehicle 305 reverses and returns to thedirection from which it approached the intersection.

Referring to FIG. 3A, the set of available actions for the uncoupledstrategy mode of the autonomous vehicle 305 may include stopping andwaiting for the second vehicle 310 to move and/or broadcast its strategymode and/or actions, flashing lights, emitting audible noises, reversingback in the direction from which the autonomous vehicle 305 approachedthe intersection, making a U-turn and returning to the direction fromwhich the autonomous vehicle 305 approached the intersection, making aright turn and moving away from the second vehicle 310, and/orcombinations thereof in accordance with the selection of the uncoupledstrategy mode for the operations of the autonomous vehicle 305.

FIG. 3B includes an autonomous vehicle 305 with an intended travel 307and a second vehicle 310 with an intended travel 312, in which theautonomous vehicle 305 may select a permissive strategy mode 104. Thesecond vehicle 310 may be an autonomous or non-autonomous vehicletraveling at a higher speed than the autonomous vehicle 305. As shown inFIG. 3B, the autonomous vehicle 305 may detect the second vehicle 310and its strategy mode and/or actions. For example, the autonomousvehicle 305 may detect the intended travel 312 of the second vehicle 310as it passes by the autonomous vehicle 305. Because the autonomousvehicle 305 is traveling at a slower speed than that of the secondvehicle 310, the autonomous vehicle 305 may select a permissive strategymode and continue its current actions so as to permit the operations bythe second vehicle 310.

Referring to FIG. 3B, the set of available actions for the permissivestrategy mode of the autonomous vehicle 305 may include continuing tostop and wait until the second vehicle 310 passes by the autonomousvehicle 305, continuing to slowly make a left turn through theintersection such that the second vehicle 310 passes by the autonomousvehicle 305 before the autonomous vehicle 305 makes the left turn, orother continued operations so as to permit or allow the operations ofthe second vehicle 310 without change to the operations of theautonomous vehicle 305.

FIG. 3C includes an autonomous vehicle 305 with an intended travel 307and a second vehicle 310 with an intended travel 312, in which theautonomous vehicle 305 may select an assistive strategy mode 106. Thesecond vehicle 310 may be an autonomous or non-autonomous vehicletraveling at a similar speed as the autonomous vehicle 305. As shown inFIG. 3C, the autonomous vehicle 305 may detect the second vehicle 310and its strategy mode and/or actions. For example, the autonomousvehicle 305 may detect the intended travel 312 of the second vehicle 310as it proceeds into the intersection. Because the autonomous vehicle 305is traveling at a similar speed as the second vehicle 310 and mayrecognize a conflict between the operations of the second vehicle 310and its own operations, the autonomous vehicle 305 may select anassistive strategy mode to facilitate the operations by the secondvehicle 310.

Referring to FIG. 3C, the set of available actions for the assistivestrategy mode of the autonomous vehicle 305 may include accelerating tomake a left turn before the second vehicle proceeds into theintersection, decelerating to allow the second vehicle to pass throughthe intersection before making a left turn, stopping and waiting for thesecond vehicle to pass through the intersection, flashing lights,emitting audible noises, altering or modifying the intended travel 307of the autonomous vehicle 305 to avoid a conflict with the secondvehicle 310, and/or combinations thereof so as to facilitate theoperations of the second vehicle 310 by changing the operations of theautonomous vehicle 305.

FIG. 3D includes an autonomous vehicle 305 with an intended travel 307,and a second vehicle 310 with an intended travel 312, in which theautonomous vehicle 305 may select a preventative strategy mode 108. Thesecond vehicle 310 may be an autonomous or non-autonomous vehicle. Asshown in FIG. 3D, the autonomous vehicle 305 may detect the secondvehicle 310 and its strategy mode and/or actions. For example, theautonomous vehicle 305 may detect the intended travel 312 of the secondvehicle 310 as it seeks to proceed straight through the intersection andpast the autonomous vehicle 305 toward the direction from which theautonomous vehicle 305 arrived at the intersection. However, theautonomous vehicle 305 may have additional information related to theintended travel 312 of the second vehicle 310, such as the presence ofan accident, construction, traffic congestion, or other incident thatshould be avoided. Because the autonomous vehicle 305 may recognize aconflict between the operations of the second vehicle 310 and theadditional information related to the direction of intended travel 312,the autonomous vehicle 305 may select a preventative strategy mode toprevent the operations by the second vehicle 310.

Referring to FIG. 3D, the set of available actions for the preventativestrategy mode of the autonomous vehicle 305 may include performing aU-turn to block the intended travel 312 of the second vehicle 310,changing lanes to block the intended travel 312 of the second vehicle310, reversing to block the intended travel 312 of the second vehicle310, flashing lights, emitting audible noises, and/or combinationsthereof so as to prevent the operations of the second vehicle 310 bychanging the operations of the autonomous vehicle 305. Further, in someembodiments, an autonomous vehicle 305 operating in a preventativestrategy mode may not have direct physical access to the second vehicle310, e.g., the autonomous vehicle 305 is on a roadway from which thereis no direct access to the roadway on which the second vehicle 310 istraveling, even if the two roadways are close to or pass each other.However, even in such scenarios, if the autonomous vehicle 305 hasadditional information related to the intended travel 312 of the secondvehicle 310, such as an incident that should be avoided, the autonomousvehicle 305 may select from a set of available actions for thepreventative strategy mode to alert the second vehicle 310, e.g.,flashing lights or emitting audible noises.

FIG. 4A is a schematic diagram of the four strategy modes 102, 104, 106,108 for autonomous vehicle operations in a third example environment400, according to an implementation. The third example environment 400includes a parking lot in which autonomous and non-autonomous vehiclesmay perform various actions such as entering the parking lot, leavingthe parking lot, starting, stopping, changing directions, making turns,reversing, entering a parking space, leaving a parking space, or otheractions. The third example environment 400 includes various parkingspaces 401-1, 401-2, 401-3, 401-4, 401-5, 401-6, 401-7, 401-8, 401-9,401-10, 401-11, 401-12, some of which are occupied by parked vehicles403-1, 403-2, 403-3, 403-5, 403-6, 403-7, 403-8, 403-10, 403-11.

FIG. 4A includes an autonomous vehicle 405 entering the parking lot anda second vehicle 410 operating within the parking lot, in which theautonomous vehicle 405 may select any of the four strategy modes 102,104, 106, 108 depending on the detected strategy mode and/or actions ofthe second vehicle 410 and/or other vehicles in the parking lot. Thesecond vehicle 410 may be an autonomous or non-autonomous vehicle.

As shown in FIG. 4A, the autonomous vehicle 405 may not be able todetect the strategy mode and/or actions of the second vehicle 410. Forexample, the second vehicle 410 may be stopped within the parking lotand may not be broadcasting or transmitting its strategy mode or anyother information. As a result, the autonomous vehicle 405 may select anuncoupled strategy mode and any corresponding action that does notaffect or is not affected by the second vehicle 410. Various exampleactions that the autonomous vehicle 405 may take are shown as travelarrows 402-1, 402-2, 402-3. For example, the autonomous vehicle 405 mayreverse out of the parking lot along arrow 402-1. In addition, theautonomous vehicle 405 may make a U-turn and leave the parking lot alongarrow 402-2. Further, the autonomous vehicle 405 may proceed to parkingspace 401-12 along arrow 402-3. Other available uncoupled actions mayinclude stopping and waiting for the second vehicle 410 to move and/orbroadcast its strategy mode and/or actions, flashing lights, emittingaudible noises, and/or combinations of any of the above in accordancewith the selection of the uncoupled strategy mode for the operations ofthe autonomous vehicle 405.

Referring to FIG. 4A again, the autonomous vehicle 405 may detect thesecond vehicle 410 and its strategy mode and/or actions. For example,the autonomous vehicle 405 may detect the intended travel 412 of thesecond vehicle 410. The intended travel 412 may include any of variousactions such as proceeding straight 412-1 past the autonomous vehicle405, turning right 412-2 into parking space 401-9, or turning left 412-3into parking space 401-4. As a result of any of the various intendedtravels 412 of the second vehicle 410, the autonomous vehicle 405 mayselect a permissive strategy mode and continue its current actions so asto permit the operations by the second vehicle 410. One example actionthat the autonomous vehicle 405 may take is shown as travel arrow 404.For example, the autonomous vehicle 405 may continue straight alongtravel arrow 404 at a relatively slow speed that would allow the secondvehicle 410 to perform any of the various intended travels 412-1, 412-2,412-3 without conflict or interference from the autonomous vehicle 405.Other available permissive actions may include continuing to stop andwait until the second vehicle 410 completes any of the various intendedtravels 412, continuing to make a left turn into parking space 401-12while the second vehicle 410 proceeds to a parking space along intendedtravels 412-2 or 412-3 or before the second vehicle 410 reaches theautonomous vehicle 405 along intended travel 412-1, or other continuedoperations so as to permit or allow the operations of the second vehicle410 without change to the operations of the autonomous vehicle 405.

Referring to FIG. 4A yet again, the autonomous vehicle 405 may detectthe second vehicle 410 and its strategy mode and/or actions. Forexample, the autonomous vehicle 405 may detect the intended travel 412-3of the second vehicle 410 as it proceeds to turn left into parking space401-4. Because the autonomous vehicle 405 is traveling at a similarspeed as the second vehicle 410 and may recognize a conflict between theoperations of the second vehicle 410 and its own operations, theautonomous vehicle 405 may select an assistive strategy mode tofacilitate the operations by the second vehicle 410. Two example actionsthat the autonomous vehicle 405 may take are shown as travel arrows406-1, 406-2. For example, the autonomous vehicle 405 may continuestraight at a reduced speed along travel arrow 406-1 that wouldfacilitate the intended travel 412-3 of the second vehicle 410 beforethe autonomous vehicle 405 reached the parking space 401-4. In addition,the autonomous vehicle 405 may continue straight at an increased speedalong travel arrow 406-2 that would facilitate the intended travel 412-3of the second vehicle 410 after the autonomous vehicle 405 passed theparking space 401-4. Other available assistive actions may includestopping and waiting for the second vehicle to enter the parking space401-4, flashing lights, emitting audible noises, otherwise altering ormodifying the intended travel of the autonomous vehicle 405 to avoid aconflict with the second vehicle 410, and/or combinations thereof so asto facilitate the operations of the second vehicle 410 by changing theoperations of the autonomous vehicle 405.

Referring to FIG. 4A once again, the autonomous vehicle 405 may detectthe second vehicle 410 and its strategy mode and/or actions. Forexample, the autonomous vehicle 405 may detect the intended travel 412-1of the second vehicle 410 as it seeks to proceed straight through theparking lot. However, the autonomous vehicle 405 may have additionalinformation related to other vehicles or aspects of the environment,e.g., a reversing movement of vehicle 403-11 out of parking space401-11, a movement of a pedestrian from a vicinity of vehicle 403-11, orany other incident that should be avoided. Because the autonomousvehicle 405 may recognize a conflict between the operations of thesecond vehicle 410 and the additional information related to thedirection of intended travel 412-1, the autonomous vehicle 405 mayselect a preventative strategy mode to prevent the operations by thesecond vehicle 410. One example action that the autonomous vehicle maytake is shown as travel arrow 408-1. For example, the autonomous vehicle405 may block the movement of the second vehicle 410 along intendedtravel 412-1 so as prevent an incident, e.g., a collision with vehicle403-11 reversing out of parking space 401-11, or a contact with apedestrian moving out from a vicinity of vehicle 403-11.

As another example, the autonomous vehicle 405 may detect the intendedtravel 412-3 of the second vehicle 410 as it proceeds to turn left intoparking space 401-4. However, the autonomous vehicle 405 may haveadditional information related to other vehicles or aspects of theenvironment, e.g., a reversing movement of vehicle 403-3 out of parkingspace 401-3, a movement of a pedestrian from a vicinity of vehicle403-3, or any other incident that should be avoided. Because theautonomous vehicle 405 may recognize a conflict between the operationsof the second vehicle 410 and the additional information related to thedirection of intended travel 412-3, the autonomous vehicle 405 mayselect a preventative strategy mode to prevent the operations by thesecond vehicle 410. One example action that the autonomous vehicle maytake is shown as travel arrow 408-2. For example, the autonomous vehicle405 may block the movement of the second vehicle 410 along intendedtravel 412-3 so as prevent an incident, e.g., a collision with vehicle403-3 reversing out of parking space 401-3, or a contact with apedestrian moving out from a vicinity of vehicle 403-3.

Other available preventative actions may include blocking a movement ofthe second vehicle 410, blocking a movement of any other vehicle 403,blocking a movement of a pedestrian, flashing lights, emitting audiblenoises, and/or combinations thereof so as to prevent the operations ofthe second vehicle 410 by changing the operations of the autonomousvehicle 405.

FIG. 4B is a schematic diagram of the four strategy modes 102, 104, 106,108 for autonomous vehicle operations in a fourth example environment420, according to an implementation. The fourth example environment 420includes a warehouse, distribution facility, manufacturing facility, orother facility in which autonomous and non-autonomous vehicles andsystems may perform various actions such as receiving items, sortingitems, storing items, retrieving items, packing items, transferringitems, manufacturing items, assembling items, or other actions. Thefourth example environment 420 includes various storage areas,processing stations or areas, aisles, or other areas.

FIG. 4B includes an autonomous vehicle 425 traversing an aisle withinthe facility, a human worker or operator 430 traversing another aislewithin the facility, and a robotic or mechatronic arm or system 435situated within the facility. The autonomous vehicle 425 may select anyof the four strategy modes 102, 104, 106, 108 depending on the detectedstrategy mode and/or actions of the human worker or operator 430, therobotic or mechatronic arm or system 435, and/or other operators,systems, or vehicles in the facility. The robotic or mechatronic arm orsystem 435 may be an autonomous or non-autonomous system, and therobotic or mechatronic arm or system 435 may have an associated radius437, which may be hazardous to enter by other operators, vehicles, orsystems during operation.

As shown in FIG. 4B, the autonomous vehicle 425 may not be able todetect the actions of the human worker or operator 430. For example, thehuman worker or operator 430 may have stopped within an aisle of thefacility. As a result, the autonomous vehicle 425 may select anuncoupled strategy mode and any corresponding action that does notaffect or is not affected by the human worker or operator 430. Oneexample action that the autonomous vehicle 425 may take is shown astravel arrow 422. For example, the autonomous vehicle 425 may reverseits direction and travel along an aisle away from the human worker oroperator 430. Other available uncoupled actions may include making aU-turn and leaving the area, stopping and waiting for the human workeror operator 430 to move, flashing lights, emitting audible noises,and/or combinations of any of the above in accordance with the selectionof the uncoupled strategy mode for the operations of the autonomousvehicle 425.

Referring to FIG. 4B again, the autonomous vehicle 425 may detect thehuman worker or operator 430 and its actions. For example, theautonomous vehicle 425 may detect the intended travel 432 of the humanworker or operator 430 proceeding straight along the aisle toward therobotic or mechatronic arm or system 435, which may not currently be inoperation. As a result of the intended travel 432 of the human worker oroperator 430, the autonomous vehicle 425 may select a permissivestrategy mode and continue its current actions so as to permit theoperations by the human worker or operator 430. One example action thatthe autonomous vehicle 425 may take is shown as travel arrow 424. Forexample, the autonomous vehicle 425 may continue waiting at the end ofthe aisle until the human worker or operator 430 passes by. Otheravailable permissive actions may include continuing to move slowly alongthe aisle such that the human worker or operator 430 passes by,continuing to move away from the area, or other continued operations soas to permit or allow the operations of the human worker or operator 430without change to the operations of the autonomous vehicle 425.

Referring to FIG. 4B yet again, the autonomous vehicle 425 may detectthe human worker or operator 430 and its actions. For example, theautonomous vehicle 425 may detect the intended travel 432 of the humanworker or operator 430 proceeding straight along the aisle toward therobotic or mechatronic arm or system 435, which may not currently be inoperation. Because the autonomous vehicle 425 is traveling at a similarspeed as the human worker or operator 430 and may recognize a conflictbetween the operations of the human worker or operator 430 and its ownoperations, the autonomous vehicle 425 may select an assistive strategymode to facilitate the operations by the human worker or operator 430.One example action that the autonomous vehicle 425 may take is shown astravel arrow 426. For example, the autonomous vehicle 425 may turn rightand proceed along travel arrow 426 away from the human worker oroperator 430 in order to facilitate the intended travel 432 of the humanworker or operator 430. Other available assistive actions may includestopping and waiting for the human worker or operator 430 to pass by,flashing lights, emitting audible noises, otherwise altering ormodifying the intended travel of the autonomous vehicle 425 to avoid aconflict with the human worker or operator 430, and/or combinationsthereof so as to facilitate the operations of the human worker oroperator 430 by changing the operations of the autonomous vehicle 425.

Referring to FIG. 4B once again, the autonomous vehicle 425 may detectthe human worker or operator 430 and its actions. For example, theautonomous vehicle 425 may detect the intended travel 432 of the humanworker or operator 430 proceeding straight along the aisle toward therobotic or mechatronic arm or system 435. However, the autonomousvehicle 425 may have additional information related to other operators,systems, vehicles or aspects of the environment, e.g., a start ofoperation of the robotic or mechatronic arm or system 435, or any otherincident that should be avoided. Because the autonomous vehicle 425 mayrecognize a conflict between the operations of the human worker oroperator 430 and the additional information related to the direction ofintended travel 432, e.g., a start of operation of the robotic ormechatronic arm or system 435, the autonomous vehicle 425 may select apreventative strategy mode to prevent the operations by the human workeror operator 430. One example action that the autonomous vehicle may takeis shown as travel arrow 428. For example, the autonomous vehicle 425may block the movement of the human worker or operator 430 alongintended travel 432 so as prevent an incident, e.g., a contact orcollision with the robotic or mechatronic arm or system 435 within itsradius 437 of operation.

Other available preventative actions may include redirecting or guidinga movement of the human worker or operator 430 away from the hazardousarea, blocking a movement of the robotic or mechatronic arm or system435, flashing lights, emitting audible noises, and/or combinationsthereof so as to prevent the operations of the human worker or operator430 by changing the operations of the autonomous vehicle 425.

FIG. 4C is a schematic diagram of the four strategy modes 102, 104, 106,108 for autonomous vehicle operations in a fifth example environment440, according to an implementation. The fifth example environment 440includes an airspace in which autonomous and non-autonomous vehicles andsystems may perform various actions such as transporting items orpeople, changing directions, changing speeds, changing altitudes, orother actions.

FIG. 4C includes an autonomous vehicle 445 operating within theairspace, a small passenger plane 450 traveling along intended travel452, and a large commercial airliner 455 traveling along intended travel457. The autonomous vehicle 445 may select any of the four strategymodes 102, 104, 106, 108 depending on the detected strategy mode and/oractions of the small passenger plane 450, the large commercial airliner455, and/or other operators, systems, or vehicles in the airspace. Thesmall passenger plane 450 and/or the large commercial airliner 455 maybe autonomous or non-autonomous systems.

As shown in FIG. 4C, the autonomous vehicle 445 may not be able todetect the strategy mode and/or actions of the small passenger plane450. For example, the small passenger plane 450 may be performing amaneuver that is not recognized by the autonomous vehicle 445. As aresult, the autonomous vehicle 445 may select an uncoupled strategy modeand any corresponding action that does not affect or is not affected bythe small passenger plane 450. One example action that the autonomousvehicle 445 may take is shown as travel arrow 442. For example, theautonomous vehicle 445 may change its direction and travel away from thesmall passenger plane 450. Other available uncoupled actions may includechanging altitudes, speeds, and/or directions and traveling away fromthe small passenger plane 450, stopping, hovering, and waiting for thesmall passenger plane 450 to move and/or provide its strategy modeand/or actions, flashing lights, emitting audible noises, and/orcombinations of any of the above in accordance with the selection of theuncoupled strategy mode for the operations of the autonomous vehicle445.

Referring to FIG. 4C again, the autonomous vehicle 445 may detect thesmall passenger plane 450 and its strategy mode and/or actions. Forexample, the autonomous vehicle 445 may detect the intended travel 452of the small passenger plane 450. As a result of the intended travel 452of the small passenger plane 450, the autonomous vehicle 445 may selecta permissive strategy mode and continue its current actions so as topermit the operations by the small passenger plane 450. One exampleaction that the autonomous vehicle 445 may take is shown as travel arrow444. For example, the autonomous vehicle 445 may continue hovering andwaiting until the small passenger plane 450 passes by. Other availablepermissive actions may include continuing to move slowly in a directionsuch that the small passenger plane 450 passes by, continuing to moveaway from the area, or other continued operations so as to permit orallow the operations of the small passenger plane 450 without change tothe operations of the autonomous vehicle 445.

Referring to FIG. 4C yet again, the autonomous vehicle 445 may detectthe small passenger plane 450 and its strategy mode and/or actions. Forexample, the autonomous vehicle 445 may detect the intended travel 452of the small passenger plane 450. If the autonomous vehicle 445 istraveling at a similar speed in a similar direction as the smallpassenger plane 450 and may recognize a conflict between the operationsof the small passenger plane 450 and its own operations, the autonomousvehicle 445 may select an assistive strategy mode to facilitate theoperations by the small passenger plane 450. One example action that theautonomous vehicle 445 may take is shown as travel arrow 446. Forexample, the autonomous vehicle 445 may increase speed and proceed alongtravel arrow 446 that would facilitate the intended travel 452 of thesmall passenger plane 450 behind the autonomous vehicle 445. Otheravailable assistive actions may include changing altitudes, speeds,and/or directions such that the autonomous vehicle 445 does notinterfere with the intended travel 452 of the small passenger plane 450,stopping, hovering, and waiting for the small passenger plane 450 topass by, flashing lights, emitting audible noises, otherwise altering ormodifying the intended travel of the autonomous vehicle 445 to avoid aconflict with the small passenger plane 450, and/or combinations thereofso as to facilitate the operations of the small passenger plane 450 bychanging the operations of the autonomous vehicle 445.

Referring to FIG. 4C once again, the autonomous vehicle 445 may detectthe small passenger plane 450 and its strategy mode and/or actions. Forexample, the autonomous vehicle 445 may detect the intended travel 452of the small passenger plane 450. However, the autonomous vehicle 445may have additional information related to other operators, systems,vehicles, or aspects of the environment, e.g., a large commercialairliner 455 with intended travel 457 that potentially conflicts withthe intended travel 452 of the small passenger plane 450, or any otherincident that should be avoided. Because the autonomous vehicle 445 mayrecognize a conflict between the operations of the small passenger plane450 and the additional information related to the direction of intendedtravel 452, the autonomous vehicle 445 may select a preventativestrategy mode to prevent the operations by the small passenger plane450. One example action that the autonomous vehicle may take is shown astravel arrow 448. For example, the autonomous vehicle 445 may block themovement of the small passenger plane 450 along intended travel 452 soas prevent an incident, e.g., a proximity, contact, or collision withthe large commercial airliner 455.

Other available preventative actions may include redirecting or guidinga movement of the small passenger plane 450 away from any potentialconflict, redirecting or guiding a movement of the large commercialairliner 455, flashing lights, and/or combinations thereof so as toprevent the operations of the small passenger plane 450 by changing theoperations of the autonomous vehicle 445.

In any of the various example environments shown in FIGS. 2A-2D, 3A-3D,and 4A-4C, or in any other environments, an autonomous vehicle maydetect strategy modes of one or more other vehicles. Based at least inpart on the detected strategy modes of one or more other vehicles, theautonomous vehicle may select a strategy mode for its own operation.

For example, in some embodiments, if an autonomous vehicle cannot detecta strategy mode of a second vehicle, the autonomous vehicle may selectan uncoupled strategy mode. Then, the autonomous vehicle may select anuncoupled action that does not affect or is not affected by operationsof the second vehicle. Example uncoupled actions may include stoppingand waiting, reversing, making a U-turn, or otherwise traveling awayfrom the second vehicle having the unknown strategy mode. Alternatively,if the autonomous vehicle cannot detect a strategy mode of a secondvehicle, the autonomous vehicle may select a preventative strategy modeif the autonomous vehicle has additional information that may not beavailable to the second vehicle. Then, the autonomous vehicle may selecta preventative action to prevent one or more possible actions of thesecond vehicle, e.g., proceeding toward an accident, construction zone,or other incident of which the autonomous vehicle has additionalinformation. Example preventative actions may include changing lanes,changing speeds, making a U-turn, or otherwise preventing one or morepossible actions of the second vehicle. Additional example preventativeactions may include guiding the second vehicle, e.g., an aerial vehicle,toward an airspace, holding zone, flight path, runway, circuit, orlanding pad. Further, if the autonomous vehicle cannot detect a strategymode of a second vehicle but the second vehicle begins to take anaction, the autonomous vehicle may select any of the uncoupled,permissive, assistive, or preventative strategy modes. Then, theautonomous vehicle may select an action corresponding to the selectedstrategy mode, e.g., an uncoupled action that does not affect or is notaffected by the action of the second vehicle, a permissive action thatpermits the action by the second vehicle, an assistive action thatfacilitates the action by the second vehicle, or a preventative actionthat prevents the action by the second vehicle.

In other embodiments, if an autonomous vehicle detects that a secondvehicle is operating in an uncoupled strategy mode, the autonomousvehicle may select any of the uncoupled, permissive, assistive, orpreventative strategy modes. Then, because the second vehicle isexpected to take an uncoupled action, i.e., an action that does notaffect or is not affected by operations of the autonomous vehicle, theautonomous vehicle may select an action corresponding to the selectedstrategy mode, e.g., an uncoupled action that similarly does not affector is not affected by the uncoupled action of the second vehicle, apermissive action that permits the uncoupled action by the secondvehicle, an assistive action that facilitates the uncoupled action bythe second vehicle, or a preventative action that prevents the uncoupledaction by the second vehicle, e.g., if the autonomous vehicle hasadditional information that may not be available to the second vehicle.Any of the example actions described herein may be selected based on theselected strategy mode.

In further embodiments, if an autonomous vehicle detects that a secondvehicle is operating in a permissive strategy mode, the autonomousvehicle may select any of the uncoupled, permissive, assistive, orpreventative strategy modes. Then, because the second vehicle isexpected to take a permissive action, i.e., an action that permits theoperations of the autonomous vehicle without change to the operations ofthe second vehicle, the autonomous vehicle may select an actioncorresponding to the selected strategy mode, e.g., an uncoupled actionthat does not affect or is not affected by the permissive action of thesecond vehicle, a permissive action that similarly permits thepermissive action by the second vehicle, an assistive action thatfacilitates the permissive action by the second vehicle, or apreventative action that prevents the permissive action by the secondvehicle, e.g., if the autonomous vehicle has additional information thatmay not be available to the second vehicle. Any of the example actionsdescribed herein may be selected based on the selected strategy mode. Inone particular example, if both the autonomous vehicle and the secondvehicle are operating in permissive strategy modes, each vehicle maycontinue its operations without change, e.g., stopping or hovering inplace and waiting for the other vehicle to move. Then, based at least inpart on a cost function of continuing to stop and wait, one or bothvehicles may select new strategy modes in order to resolve the currentscenario. The cost function may be related to one or more operationalgoals of one or both of the vehicles, as further described herein.

In still further embodiments, if an autonomous vehicle detects that asecond vehicle is operating in an assistive strategy mode, theautonomous vehicle may select any of the uncoupled, permissive,assistive, or preventative strategy modes. Then, because the secondvehicle is expected to take an assistive action, i.e., an action thatfacilitates the operations of the autonomous vehicle by changing theoperations of the second vehicle, the autonomous vehicle may select anaction corresponding to the selected strategy mode, e.g., an uncoupledaction that does not affect or is not affected by the assistive actionof the second vehicle, a permissive action that permits the assistiveaction by the second vehicle, an assistive action that similarlyfacilitates the assistive action by the second vehicle, or apreventative action that prevents the assistive action by the secondvehicle, e.g., if the autonomous vehicle has additional information thatmay not be available to the second vehicle. Any of the example actionsdescribed herein may be selected based on the selected strategy mode. Inone particular example, if both the autonomous vehicle and the secondvehicle are operating in assistive strategy modes, each vehicle maychange its operations to facilitate operations of the other vehicle,e.g., slowing and/or stopping or hovering in place and waiting for theother vehicle to complete its operation. Then, based at least in part ona cost function of continuing to slow and/or stop and wait, one or bothvehicles may select new strategy modes in order to resolve the currentscenario. The cost function may be related to one or more operationalgoals of one or both of the vehicles, as further described herein.

In other embodiments, if an autonomous vehicle detects that a secondvehicle is operating in a preventative strategy mode, e.g., if thesecond vehicle has additional information that may not be available tothe autonomous vehicle, the autonomous vehicle may select any of theuncoupled, permissive, assistive, or preventative strategy modes. Then,because the second vehicle is expected to take a preventative action,i.e., an action that prevents one or more operations of the autonomousvehicle by changing the operations of the second vehicle, the autonomousvehicle may select an action corresponding to the selected strategymode, e.g., an uncoupled action that does not affect or is not affectedby the preventative action of the second vehicle, a permissive actionthat permits the preventative action by the second vehicle, an assistiveaction that facilitates the preventative action by the second vehicle,or a preventative action that similarly prevents the preventative actionby the second vehicle, e.g., if the autonomous vehicle has additionalinformation that may not be available to the second vehicle. Any of theexample actions described herein may be selected based on the selectedstrategy mode. In one particular example, if both the autonomous vehicleand the second vehicle are operating in preventative strategy modes,each vehicle may change its operations to prevent operations of theother vehicle, e.g., blocking an intended travel or operation of eachother, or attempting operations to guide the other vehicle toward anairspace, holding zone, flight path, runway, circuit, or landing pad.Then, based at least in part on a cost function of continuing to blockor attempt to guide each other, one or both vehicles may select newstrategy modes in order to resolve the current scenario. The costfunction may be related to one or more operational goals of one or bothof the vehicles, as further described herein.

In any of the embodiments described herein, if an autonomous vehicle isunable to select a strategy mode based on detected information aboutother vehicles and/or its environment, and/or if the autonomous vehicleis unable to select an action based on a selected strategy mode, remoteassistance may be utilized to provide direct guidance or recommendationsto the autonomous vehicle, e.g., by one or more remote, human operatorsconnected to the autonomous vehicle over a network or via the autonomousvehicle management system, as described herein. For example, the variousinformation about other vehicles and/or its environment detected by theautonomous vehicle may be provided to the remote, human operator, e.g.,via a computing device using associated output devices, and the remote,human operator may provide guidance or recommendations to the autonomousvehicle such as a suggested strategy mode and/or a suggested action,e.g., via the computing device using associated input devices.

Although FIGS. 2A-2D, 3A-3D, and 4A-4C describe particular examplestrategy modes and/or actions of various autonomous or non-autonomousvehicles, any other number or combination of autonomous ornon-autonomous vehicles, strategy modes, and/or actions may interact invarious example environments. For example, more than two vehicles mayinteract along a roadway, at an intersection, in a parking lot, or inany other environments. In addition, the vehicles that are interactingin any of the various example environments or in any other environmentsmay be operating with any combination of strategy modes. Further,various other actions in addition to changing lanes, changing speeds,changing directions, making turns, starting, stopping, reversing,entering parking spaces, exiting parking spaces, or any other actionsmay be performed by various vehicles in any of the various exampleenvironments or in any other environments. For example, other actionsrelated to aerial vehicles may include taking off, landing, enteringairspaces/zones, exiting airspaces/zones, modifying flight paths,changing altitude, changing speed, changing direction, hovering orloitering in place, avoiding static or dynamic obstacles, or any otheractions associated with aerial vehicles.

Accordingly, as shown in FIGS. 2A-2D, 3A-3D, and 4A-4C, an autonomousvehicle may detect strategy modes and/or actions of other vehicles, andmay select a strategy mode based on such information. Then, theautonomous vehicle may select an action from a set of available actionscorresponding to the selected strategy mode. The set of availableactions for the selected strategy mode may include various actions orcombinations of actions, including but not limited to those describedherein, and the set of available actions may vary based on the selectedstrategy mode, as well as the particular scenarios and/or environmentswithin which the autonomous vehicle is operating.

The autonomous vehicle may also select a strategy mode and/or an actionbased on characteristics and/or capabilities of the autonomous vehicleand/or other vehicles, as further described herein. In addition, theautonomous vehicle may also select a strategy mode and/or an actionbased on aspects of the environment and/or topological constraintsassociated with the environment, as further described herein. Further,the autonomous vehicle may also select a strategy mode and/or an actionbased on one or more operational goals of the autonomous vehicle, asfurther described herein. The operational goals may include one or moreof safety, resolvability, efficiency, time, priority, throughput,on-time completion, average speed, number of incidents or conflicts,fuel or resource utilization, and/or other goals.

FIG. 5 is a flow diagram illustrating an example strategy mode selectionprocess 500 for autonomous vehicle operations, according to animplementation. The process 500 may begin by detecting one or morestrategy modes of one or more other vehicles, as at 502. For example, anautonomous vehicle may be located in a local environment together withthe other vehicles. The strategy modes of the other vehicles may bedetected using any of the various methods and structures for detectingstrategy modes, as further described herein. The process may continue bydetecting one or more actions of the one or more other vehicles, as at504. For example, the actions of the other vehicles may be detectedusing any of the various methods and structures for detecting actions,as further described herein. The process may then proceed to determiningor receiving one or more operational goals for the autonomous vehicle,as at 506. As described herein, the operational goals may include one ormore of safety, resolvability, efficiency, time, priority, throughput,on-time completion, average speed, number of incidents or conflicts,fuel or resource utilization, and/or other goals.

Based at least in part on the detected strategy modes of other vehicles,the detected actions of other vehicles, and/or the operational goals ofthe autonomous vehicle, a strategy mode may be selected for theautonomous vehicle, as at 508. As described herein, the strategy modemay be one of an uncoupled strategy mode, a permissive strategy mode, anassistive strategy mode, or a preventative strategy mode. Then, based atleast in part on the selected strategy mode, one or more actions may beselected for the autonomous vehicle, as at 510. As described herein, theset of available actions may include various actions or combinations ofactions based at least in part on the selected strategy mode. Theprocess 500 may then end at 512.

The process 500 may repeat continuously or intermittently. For example,the autonomous vehicle may monitor for changes to the strategy modes oractions of other vehicles, changes to its operational goals, and/orchanges to the other vehicles that are located in the local environmentwith the autonomous vehicle. Based on any changes to the other vehiclesor the operational goals, the autonomous vehicle may select a newstrategy mode and corresponding actions.

FIG. 6 is a schematic diagram of coordinated optimization of strategymodes 600 for autonomous vehicle operations in a first exampleenvironment, according to an implementation. The first exampleenvironment includes three different roadways 610, 620, 630 thatgenerally traverse from a similar starting point to a similar endingpoint. The first roadway 610 may be a two-lane roadway, e.g., anarterial road or freeway on which vehicles may travel at a medium speed,the second roadway 620 may be a three-lane roadway, e.g., a highway orexpressway on which vehicles may travel at a high speed, and the thirdroadway 630 may be a one-lane roadway, e.g., a local road or street onwhich vehicles may travel at a slow speed. In addition, the secondroadway 620 may include an accident, construction zone, or otherincident 650, that is causing traffic congestion on the second roadway620. Further, the third roadway 630 may also include an intersection 632having stop signs or traffic lights and a low speed zone 636, e.g., aschool zone near a school 634. Various types of autonomous andnon-autonomous vehicles 601 may utilize any of the three roadways 610,620, 630.

An autonomous vehicle management system 640 may be in communication withone or more of the vehicles 601, as well as an autonomous vehicle 605that is currently located at a starting point associated with the threeroadways 610, 620, 630. The autonomous vehicle management system 640 mayreceive information from the vehicles 601, including strategy modes,actions, operational goals, aspects of the environment, and topologicalconstraints. The autonomous vehicle management system 640 may alsoreceive information from the autonomous vehicle 605, including itsstrategy mode, actions, operational goals, and potentially otherinformation associated with the autonomous vehicle 605. The aspects ofthe environment may include elements in the environment such as roadsigns, traffic signals, pedestrians, cyclists, animals, trees,buildings, obstructions, natural or artificial obstacles, human-operatedvehicles, disabled or malfunctioning vehicles, dynamic environmentalconditions such as wind/gusts, rain, snow, ice, or pressure, or otherelements. The topological constraints may include roadway or pathwaycharacteristics or conditions, weather associated the environment, orcharacteristics or capabilities of the vehicles. In addition, thetopological constraints may be determined based at least in part on theaspects of the environment. The aspects of the environment and thetopological constraints may be used to determine or filter the set ofavailable actions associated with a selected strategy mode for anautonomous vehicle. The operational goals may include one or more ofsafety, resolvability, efficiency, time, priority, throughput, on-timecompletion, average speed, number of incidents or conflicts, fuel orresource utilization, and/or other goals.

The autonomous vehicle management system 640 may also determine orreceive one or more operational goals for the system as a whole. Theoperational goals for the system may include one or more of safety,resolvability, efficiency, time, priority, throughput, on-timecompletion, average speed, number of incidents or conflicts, fuel orresource utilization, load distribution across the system, riskdistribution across the system, and/or other goals. The autonomousvehicle management system 640 may then process all the informationreceived from the vehicles 601 and the autonomous vehicle 605, whilealso taking into account the operational goals for the system. Theautonomous vehicle management system 640 may then instruct modificationsto operations of one or more autonomous vehicles in order to achieve theoperational goals for the system.

For example, as shown in FIG. 6, the autonomous vehicle managementsystem 640 may receive information from the vehicles 601 regardingstrategy modes, actions, operational goals, aspects of the environment,and topological constraints. In particular, the autonomous vehiclemanagement system 640 may receive information regarding the incident 650on the second roadway 620. Based at least in part on the operationalgoals of the system, the autonomous vehicle management system 640 mayinstruct modifications to operations of the autonomous vehicle 605 thatseeks to traverse one of the roadways 610, 620, 630, as well as one ormore other autonomous vehicles.

In some embodiments, if the autonomous vehicle 605 includes anoperational goal related to time, e.g., to traverse one of the roadways610, 620, 630 by a certain time, the autonomous vehicle 605 may beinstructed to traverse first roadway 610 or third roadway 630 to avoidthe incident 650. If, however, the autonomous vehicle 605 has anoperational goal indicating a lower priority than other autonomousvehicles, the autonomous vehicle may be instructed to proceed alongsecond roadway 620 despite the presence of incident 650, in order toallow other autonomous vehicles to utilize first and third roadways 610,630 to meet their operational goals. Alternatively, if an autonomousvehicle 605 is an emergency vehicle that includes an operational goal toassist at the scene of the incident 650, the autonomous vehicle 605 maybe instructed to traverse second roadway 620 and other autonomousvehicles on second roadway 620 may be instructed to assist theautonomous vehicle 605, e.g., move to the shoulder or block othervehicles, so that the autonomous vehicle 605 may reach the incident 650.

In other embodiments, if the autonomous vehicle management system 640includes an operational goal related to average speed or loaddistribution across the system, the autonomous vehicle 605 may beinstructed to traverse first roadway 610 or third roadway 630, and otherautonomous vehicles may be instructed to traverse one of roadways 610,620, 630 to achieve the operational goals of the system. If, however,the autonomous vehicle management system 640 includes an operationalgoal related to throughput, safety, or number of incidents or conflicts,the autonomous vehicle 605 and other autonomous vehicles may beinstructed to traverse first roadway 610 and not traverse third roadway630, in order to move vehicles more quickly through system along firstroadway 610 and avoid potential conflicts or safety issues atintersection 632 or at low speed zone 636, as well as to avoid theincident 650 on the second roadway 620.

The operational goals of the autonomous vehicle management system 640and the operational goals of each of the autonomous vehicles, such asautonomous vehicle 605, may be considered by the autonomous vehiclemanagement system 640 in order to optimally meet all operational goalsof the system, as well as operational goals of each of the autonomousvehicles operating within the system.

FIG. 7 is a schematic diagram of coordinated optimization of strategymodes 700 for autonomous vehicle operations in a second exampleenvironment, according to an implementation. The second exampleenvironment includes three different roadways 710, 720, 730 that areconnected to each other by various other roadways and intersections. Thefirst roadway 710 may be a two-way roadway, e.g., a residential road orstreet on which vehicles may travel at a slow speed, the second roadway720 may be a two-way roadway, e.g., an arterial road on which vehiclesmay travel at a medium speed, and the third roadway 730 may be a two-wayroadway, e.g., a local road or street near commercial buildings, such asstores or restaurants 734-1, 734-2, 734-3, 734-4, on which vehicles maytravel at a medium to slow speed. In addition, the first roadway 710 mayinclude an intersection 732-2 having four-way stop signs. Further, thesecond roadway 720 may include an intersection 732-1 having a trafficsignal and be crossed by a smaller roadway having two-way stop signs733-1, 733-2. The second roadway 720 may also include a traffic incident750, e.g., an accident, construction zone, or other incident, that iscausing traffic congestion. Moreover, the third roadway 730 may includean intersection 732-3 having a traffic signal and pedestrian walkways736-1, 736-2, 736-3, 736-4 and another intersection 732-4 having atraffic signal. Various types of autonomous and non-autonomous vehicles701 may utilize any of the three roadways 710, 720, 730 and theirconnecting roadways and intersections.

An autonomous vehicle management system 740 may be in communication withone or more of the vehicles 701, as well as an autonomous vehicle 705that is currently located at intersection 732-1 and seeks to traverse toa vicinity of intersection 732-4. The autonomous vehicle managementsystem 740 may receive information from the vehicles 701, includingstrategy modes, actions, operational goals, aspects of the environment,and topological constraints. The autonomous vehicle management system740 may also receive information from the autonomous vehicle 705,including its strategy mode, actions, operational goals, and potentiallyother information associated with the autonomous vehicle 705. Theaspects of the environment may include elements in the environment suchas road signs, traffic signals, pedestrians, cyclists, animals, trees,buildings, obstructions, natural or artificial obstacles, human-operatedvehicles, disabled or malfunctioning vehicles, dynamic environmentalconditions such as wind/gusts, rain, snow, ice, or pressure, or otherelements. The topological constraints may include roadway or pathwaycharacteristics or conditions, weather associated the environment, orcharacteristics or capabilities of the vehicles. In addition, thetopological constraints may be determined based at least in part on theaspects of the environment. The aspects of the environment and thetopological constraints may be used to determine or filter the set ofavailable actions associated with a selected strategy mode for anautonomous vehicle. The operational goals may include one or more ofsafety, resolvability, efficiency, time, priority, throughput, on-timecompletion, average speed, number of incidents or conflicts, fuel orresource utilization, and/or other goals.

The autonomous vehicle management system 740 may also determine orreceive one or more operational goals for the system as a whole. Theoperational goals for the system may include one or more of safety,resolvability, efficiency, time, priority, throughput, on-timecompletion, average speed, number of incidents or conflicts, fuel orresource utilization, load distribution across the system, riskdistribution across the system, and/or other goals. The autonomousvehicle management system 740 may then process all the informationreceived from the vehicles 701 and the autonomous vehicle 705, whilealso taking into account the operational goals for the system. Theautonomous vehicle management system 740 may then instruct modificationsto operations of one or more autonomous vehicles in order to achieve theoperational goals for the system.

For example, as shown in FIG. 7, the autonomous vehicle managementsystem 740 may receive information from the vehicles 701 regardingstrategy modes, actions, operational goals, aspects of the environment,and topological constraints. In particular, the autonomous vehiclemanagement system 740 may receive information regarding the incident 750on the second roadway 720. Based at least in part on the operationalgoals of the system, the autonomous vehicle management system 740 mayinstruct modifications to operations of the autonomous vehicle 705 thatseeks to traverse one of the roadways 710, 720, 730 to a vicinity ofintersection 732-4, as well as one or more other autonomous vehicles.

In some embodiments, if the autonomous vehicle 705 includes anoperational goal related to time, e.g., to reach a vicinity ofintersection 732-4 by a certain time, the autonomous vehicle 705 may beinstructed to traverse first roadway 710 to avoid the incident 750 onthe second roadway 720 and also to avoid the traffic congestion on thethird roadway 730 associated with the commercial stores and restaurants,traffic signals, and pedestrians. If, however, the autonomous vehicle705 has an operational goal indicating a lower priority than otherautonomous vehicles, the autonomous vehicle may be instructed to proceedalong second roadway 720 despite the presence of incident 750 or alongthird roadway 730 despite the traffic congestion, in order to allowother autonomous vehicles to utilize first roadway 710 to meet theiroperational goals. Alternatively, if an autonomous vehicle 705 is anemergency vehicle that has an operational goal to assist at the scene ofthe incident 750, the autonomous vehicle 705 may be instructed totraverse second roadway 720 and other autonomous vehicles on secondroadway 720 may be instructed to assist the autonomous vehicle 705,e.g., move to the shoulder or block other vehicles, so that theautonomous vehicle 705 may reach the incident 750.

In other embodiments, if the autonomous vehicle management system 740includes an operational goal related to average speed or loaddistribution across the system, the autonomous vehicle 705 may beinstructed to traverse first roadway 710 or third roadway 730, and otherautonomous vehicles may be instructed to traverse one of roadways 710,720, 730 to achieve the operational goals of the system. If, however,the autonomous vehicle management system 740 includes an operationalgoal related to throughput, safety, or number of incidents or conflicts,the autonomous vehicle 705 and other autonomous vehicles may beinstructed to traverse first roadway 710 and not traverse second orthird roadways 720, 730, in order to move vehicles more quickly throughsystem along first roadway 710 and avoid potential conflicts or safetyissues on second or third roadways 720, 730.

The operational goals of the autonomous vehicle management system 740and the operational goals of each of the autonomous vehicles, such asautonomous vehicle 705, may be considered by the autonomous vehiclemanagement system 740 in order to optimally meet all operational goalsof the system, as well as operational goals of each of the autonomousvehicles operating within the system.

For the example environments shown in FIGS. 6 and 7, as well as otherenvironments in which multiple autonomous vehicles may operate in acoordinated manner, various scheduling, sequencing, and/or managementmethods and algorithms may be used to coordinate and resolve the variousinteractions between the vehicles. For example, in some embodiments,vehicles traveling on one or more of the various roadways 610, 620, 630,710, 720, 730 may coordinate their operations using ad-hoc, peer-to-peercommunications, e.g., on the roadway 620 of FIG. 6, the vehicles mayutilize peer-to-peer communication algorithms in order to schedule andsequence the vehicles starting from the incident 650 and communicatingback to other vehicles that are approaching the incident. In otherembodiments, vehicles traveling on one or more of the roadways 610, 620,630, 710, 720, 730 may provide information to an autonomous vehiclemanagement system and receive scheduling and sequencing instructionsfrom the autonomous vehicle management system. In still otherembodiments, combinations of different scheduling, sequencing, and/ormanagement methods and algorithms may be used. For example, anautonomous vehicle management system may provide global- or fleet-levelrecommendations or suggestions to one or more autonomous vehicles to useparticular types of roadways, but then allow the one or more autonomousvehicles to operate in a peer-to-peer mode with other vehicles in orderto resolve local interactions with each other. Moreover, although FIGS.6 and 7 illustrate ground-based vehicles, similar scheduling,sequencing, and/or management methods and algorithms may be used inother environments with other types of autonomous vehicles, e.g.,air-based vehicles, water-based vehicles, space-based vehicles,automated guided vehicles, robotic vehicles or systems, or any otherenvironments in which autonomous and non-autonomous vehicles and systemsmay interoperate.

Each environment in which a vehicle may operate may be associated with atopological number. The topological number may be a value or measurerelated to the available degrees of freedom of movement of the vehiclewithin the environment. For example, a maximum topological number offour may be associated with an environment in which a vehicle hasfreedom of movement along four axes, i.e., x, y, z, and time. An aerialvehicle operating in an airspace that is free of obstacles along allfour axes may be operating in an environment associated with atopological number at or close to four. Similarly, a submarine operatingin a body of water that is free of obstacles along all four axes mayalso be operating in an environment associated with a topological numberat or close to four. Likewise, a spacecraft operating in space that isfree of obstacles along all four axes may also be operating in anenvironment associated with a topological number at or close to four.However, a ground-based vehicle generally is not capable of movementalong the z-axis, e.g., a vertical direction, and thus, a ground-basedvehicle operating in an open area that is free of obstacles may beoperating in an environment associated with a topological number at orclose to three.

Topological constraints associated with an environment may be anyaspects or elements of the environment that may reduce the topologicalnumber associated with the environment. For example, tall buildings,natural obstacles or obstructions, the ground, other aircraft, pathwaycharacteristics, birds, high winds, weather formations, or other aspectsor elements of the environment, as well as characteristics orcapabilities of the aerial vehicle, may comprise topological constraintsthat may reduce a topological number associated with an airspace inwhich an aerial vehicle is operating. Similarly, natural obstacles orobstructions, the floor of the body of water, the surface of the body ofwater, other watercraft, pathway characteristics, water creatures, highcurrents, weather formations, or other aspects or elements of theenvironment, as well as characteristics or capabilities of thesubmarine, may comprise topological constraints that may reduce atopological number associated with a body of water in which a submarineis operating. Likewise, space debris, other objects in space, planets,suns, other spacecraft, pathway characteristics, or other aspects orelements of the environment, as well as characteristics or capabilitiesof the spacecraft, may comprise topological constraints that may reducea topological number associated with a space in which a spacecraft isoperating. Furthermore, roadways, curbs, buildings, natural obstacles orobstructions, trees, other ground-based vehicles, roadwaycharacteristics, cyclists, pedestrians, land animals, weatherformations, or other aspects or elements of the environment, as well ascharacteristics or capabilities of the ground-based vehicle, maycomprise topological constraints that may reduce a topological numberassociated with an area in which a ground-based vehicle is operating.

The topological number of an environment may be further reduced based onthe characteristics or capabilities of the vehicle itself. For example,even if an aerial vehicle, a submarine, or a spacecraft is operating inan environment with a topological number at or close to four, if theaerial vehicle, submarine, or spacecraft is not capable or designed formovement in a particular direction, the actual topological numberassociated with the operations of the vehicle in the environment may bereduced, e.g., from a topological number at or close to four to atopological number at or close to three. Likewise, the characteristicsor capabilities of ground-based vehicles may impose further topologicalconstraints on the operation of ground-based vehicles within variousenvironments. Such characteristics or capabilities of ground-basedvehicles (and other types of vehicles) may include maximum acceleration,maximum deceleration, maximum speed, maximum torque, stopping distance,weight, turning radius, available fuel or power, and/or othercharacteristics or capabilities of ground-based vehicles (and othertypes of vehicles). In contrast, other characteristics or capabilitiesof ground-based vehicles (or other types of vehicles) may reduce orremove topological constraints associated with operation of ground-basedvehicles (or other types of vehicles) within particular environments,e.g., off-road capabilities that may allow ground-based vehicles totravel in areas without defined roadways or to traverse over curbs orsmall obstacles.

In addition, the topological number of an environment may further bebased on any other significant or salient elements within theenvironment, as well as respective poses, e.g., position andorientation, and changes in poses of such significant elements.Moreover, the topological number may change over time based onrespective states of such significant elements. For example, the changesto the significant elements may include presence or absence of suchelements, and/or changes to their respective poses, e.g., positionand/or orientation, over time. Examples of such significant elements mayinclude pathways or aspects thereof, natural or artificial obstacles, orany other movable or changeable elements within various environments.

Accordingly, aspects of the environment, and topological constraintsassociated with the environment, which may be determined based at leastin part on the detected aspects of the environment, may be utilized inthe selection of strategy modes by autonomous vehicles, and may also beutilized in the coordinated optimization of autonomous vehicleoperations by autonomous vehicle management systems, as describedherein.

Although FIGS. 6 and 7 include coordinated optimization of strategymodes for autonomous vehicle operations in example environments based oncurrent incidents or current information related to strategy modes,actions, operational goals, aspects of the environment, and topologicalconstraints, the autonomous vehicle management systems 640, 740described herein may also receive, store, process, and analyzehistorical information related to strategy modes, actions, operationalgoals, aspects of the environment, and topological constraints, andutilize the historical information to instruct modifications tooperations of autonomous vehicles. For example, the historicalinformation may include information about incidents, strategy modes,actions, aspects of the environment, and topological constraintsassociated with particular times of a day, particular days of a week,particular months, particular holidays, particular seasons, particularevents, particular weather patterns, particular construction or roadwayactivities, etc. that may be leveraged to instruct modifications tooperations of autonomous vehicles in order to achieve operational goalsof the system.

In addition, current or historical information related to strategymodes, actions, operational goals, aspects of the environment, andtopological constraints associated with a particular environment mayalso be utilized to instruct modifications to operations of autonomousvehicles in other similar environments, even in the absence of suchcurrent or historical information received directly from such similarenvironments. Moreover, current or historical information related tostrategy modes, actions, operational goals, aspects of the environment,and topological constraints associated with particular combinations ofstrategy modes, actions, and/or operational goals may also be utilizedto instruct modifications to operations of autonomous vehicles in othersimilar combinations of strategy modes, actions, and/or operationalgoals, even in the absence of such current or historical informationreceived directly from such similar combinations.

FIG. 8 is a flow diagram illustrating an example coordinatedoptimization process 800 for autonomous vehicle operations, according toan implementation. The process 800 may begin by receiving strategy modesfrom a plurality of vehicles, as at 802. For example, variouscombinations of vehicles may be operating in various environments, andone or more of the various vehicles may provide information related totheir strategy modes to an autonomous vehicle management system. Theprocess may continue by receiving actions from the plurality ofvehicles, as at 804. For example, various combinations of vehicles maybe operating in various environments, and one or more of the variousvehicles may provide information related to their actions to anautonomous vehicle management system. The process may then proceed toreceiving aspects of the environment detected by the plurality ofvehicles, as at 806. For example, various combinations of vehicles maybe operating in various environments, and one or more of the variousvehicles may provide information related to their environments to anautonomous vehicle management system. The aspects of the environments ofthe vehicles may be detected using any of the various methods andstructures for detecting aspects of the environments, as furtherdescribed herein. The process may continue by receiving or determiningtopological constraints associated with the environments of theplurality of vehicles, as at 808. The topological constraints associatedwith the environments of the vehicles may be determined based at leastin part on the detected aspects of the environments, as furtherdescribed herein. The process may then proceed to determining orreceiving one or more operational goals for the system, as at 810. Asdescribed herein, the operational goals for the system may include oneor more of safety, resolvability, efficiency, time, priority,throughput, on-time completion, average speed, number of incidents orconflicts, fuel or resource utilization, load distribution across thesystem, risk distribution across the system, and/or other goals.

Based at least in part on the operational goals of the system, theautonomous vehicle management system may process the received ordetermined information related to strategy modes, actions, aspects ofthe environment, and topological constraints from the plurality ofvehicles, including both current and historical information, as at 812.Then, based at least in part on the processed information, one or moremodifications to operations of autonomous vehicles may be instructed toachieve the operational goals of the system, as at 814. The process 800may then end at 816.

The process 800 may repeat continuously or intermittently. For example,the autonomous vehicle management system may monitor for changes orupdated information related to the strategy modes or actions of theplurality of vehicles, changes or updated information related to aspectsof the environment or topological constraints, and/or changes tooperational goals of the system. Based on any changes to the received ordetermined information or the operational goals of the system, theautonomous vehicle management system may instruct further modificationsto operations of the autonomous vehicles in order to achieve theoperational goals of the system.

FIG. 9 is a block diagram of an example autonomous vehicle 900,according to an implementation. The autonomous vehicle 900 may include abody 905, a propulsion mechanism 910, a steering mechanism 915, and acontrol system 950. The body 905 may include a frame, body panels, doorsor other closures, and/or other portions that form the structure of thevehicle 900. The propulsion mechanism 910 may include one or more powersupplies (not shown) and one or more engines, motors, generators, orother propulsion mechanisms to provide thrust and/or acceleration to thevehicle 900, e.g., via the wheels that are rotated by the propulsionmechanism 910. The steering mechanism 915 may include one or morecomponents that can change a direction of travel of the vehicle 900,e.g., by changing an axis of rotation of one or more wheels. The vehicle900 may also include other mechanisms or systems not illustrated in FIG.9, such as a braking or stopping mechanism, a communication system,processors, memories, input/output devices, and/or other mechanisms orsystems.

The control system 950 may be in wired or wireless communication withthe propulsion mechanism 910, the steering mechanism 915, and any othercomponents of the vehicle 900. The control system 950 may generallycontrol the operation, routing, navigation, and communication of thevehicle 900, as further described herein. Although FIG. 9 shows anexample ground-based autonomous vehicle 900, other ground-basedvehicles, air-based vehicles, water-based vehicles, space-basedvehicles, automated guided vehicles, or other robotic vehicles orsystems, or any other types of vehicles may utilize the systems andmethods described herein with respect to selection of strategy modes,coordinated optimization of autonomous vehicle operations, broadcast ofselected strategy modes, selection of strategy modes based on localsurroundings, or any other aspects described herein.

The autonomous vehicle 900 may include various broadcast or notificationdevices 920, 922 to broadcast a selected strategy mode, or otherinformation. For example, the notification devices may include one ormore lights 920, such as headlights 920-1, 920-2, turning signal lights920-3, 920-4, stop lights 920-5, or other lights. The lights 920 may beused by the autonomous vehicle 900 to broadcast a selected strategymode, or other information. For example, the lights 920 may be operatedin a particular manner, e.g., illuminated for a defined period of time,flashed a defined number of times, operated in sequences or patterns, oroperated in other manners, to notify other vehicles of a selectedstrategy mode or other information for the autonomous vehicle 900. WhileFIG. 9 shows a particular number and arrangement of lights 920, theautonomous vehicle 900 may include any other number or arrangement oflights 920.

The notification devices 922 may also include various types of devices,such as visual notification devices, audio notification devices, orsignal notification devices to broadcast a selected strategy mode, orother information. For example, the autonomous vehicle 900 may includevarious notification devices 922-1, 922-2, 922-3, 922-4, 922-5, 922-6,922-7 arranged around the vehicle 900. While FIG. 9 shows a particularnumber and arrangement of notification devices 922, the autonomousvehicle 900 may include any other number or arrangement of notificationdevices 922.

Visual notification devices 922 may include visual identifiers, licenseplates, displays, colors, symbols, or other identifiers on one or moresurfaces or locations of the vehicle. The visual notification devicesmay encode, indicate, or display information related to a selectedstrategy mode, or other information. In addition, the lights 920 mayalso form a part of the visual notification devices 922. Further, theautonomous vehicle 900 may perform defined motions that may also serveas visual identifiers. The visual notification devices may or may not bevisible to humans, and/or may or may not be discernible or decipherableby humans.

Audio notification devices 922 may include speakers, horns, sirens, orother sound or noise-emitting devices at one or more locations of thevehicle. The audio notification devices may emit sounds or noises thatencode, indicate, or broadcast information related to a selectedstrategy mode, or other information. The sounds or noises emitted by theaudio notification devices may or may not be audible to humans, and/ormay or may not be discernible or decipherable by humans.

Signal notification devices 922 may include transponders, radiofrequencytransmitters, or other electromagnetic signal transmitters at one ormore locations of the vehicle. The signal notification devices may emitor transmit signals that encode, indicate, or broadcast informationrelated to a selected strategy mode, or other information. The signalsemitted by the signal notification devices may or may not be detectableby humans, and/or may or may not be discernible or decipherable byhumans.

By using one or more of the broadcast or notification devices 920, 922,the autonomous vehicle 900 may broadcast, emit, or transmit a selectedstrategy mode, or other information. The broadcast or emittedinformation may not require any coordinated communication, e.g., ahandshake or negotiation, with other vehicles that may receive thebroadcast or emitted information.

In addition to the selected strategy mode, the autonomous vehicle 900may broadcast, emit, or transmit various other types of information,such as a time of broadcast, a position of the vehicle, a speed orvelocity of the vehicle, a priority of the vehicle, a state of thevehicle, capabilities of the vehicle, an identity of the vehicle, avehicle type of the vehicle, or any other information.

In some embodiments, the selected strategy mode and/or other informationmay be permanently encoded within an identifier or signal broadcast by anotification device, e.g., a visual identifier such as a barcode,license plate, an alphanumeric identification number, a color, or asymbol, arranged at one or more locations on the vehicle. As a result,the vehicle may always operate in the selected strategy mode, and/orother unchanging information may be permanently encoded in theidentifier, such as a priority of the vehicle, capabilities of thevehicle, an identity of the vehicle, or a vehicle type of the vehicle.

In other embodiments, the selected strategy mode and/or otherinformation may be selectively encoded within an identifier or signalbroadcast by a notification device, e.g., a visual identifier displayedon a display, an audio identifier emitted by a speaker, or a signalbroadcast by a transponder or an electromagnetic or radiofrequencytransmitter, arranged at one or more locations on the vehicle. As aresult, the vehicle may encode an identifier based on the selectedstrategy mode, and other information that is to be transmitted,including information that may change during operation of the vehicle,such as a time of broadcast, a position of the vehicle, a speed orvelocity of the vehicle, a priority of the vehicle, a state of thevehicle, or capabilities of the vehicle. In addition, if an autonomousvehicle can change its type, e.g., from an air-based vehicle to aground-based vehicle, the vehicle type of the vehicle may alsoconstitute changeable information that may be encoded in an identifier.

The encoding of the identifiers with information related to a selectedstrategy mode or other information may follow a defined standard orprotocol, and the defined standard or protocol may vary based onlocality, region, state, country, local customs, laws, or regulations,and/or other factors. For example, when using the lights 920 as visualnotification devices, a particular number of flashes of one or morelights, particular sequences or durations of illumination, particularfrequencies or wavelengths of illumination, and/or combinations thereofmay be used to encode information related to a selected strategy mode orother information. When using visual notification devices 922 such asidentifiers, barcodes, alphanumeric identifiers, license plates, orsymbols, particular characters or symbols, or particular sequences ofcharacters or symbols, may be used to encode information related to aselected strategy mode or other information. When using visualnotification devices 922 such as colors at one or more locations of thevehicle, the particular values of colors, e.g., values on ared-green-blue (RGB) scale or other color scale, may be used to encodeinformation related to a selected strategy mode or other information.When using visual notification devices 922 such as defined motions ofthe vehicle, particular types, speeds, accelerations, and/or sequencesof motions of the vehicle, or motions of components of the vehicle, maybe used to encode information related to a selected strategy mode orother information.

In addition, when using audio notification devices 922, a particularnumber of emitted sounds, particular sequences or durations of emittedsounds, particular frequencies of emitted sounds, and/or combinationsthereof may be used to encode information related to a selected strategymode or other information. Further, when using signal notificationdevices 922, particular sequences of data, e.g., digitally encoded data,may be encoded in the emitted signal and may be used to transmitinformation related to a selected strategy mode or other information.

In other embodiments, notification devices may at least partiallyrandomly encode information within visual identifiers, audio signals, orother emitted signals. For example, one or more portions of such visualidentifiers, audio signals, or emitted signals may include a randomlygenerated number, string, or component, which may be used to resolvepotential conflicts between autonomous and/or non-autonomous vehicles.As an example, if two autonomous vehicles are both operating inuncoupled strategy modes and are each waiting for the other vehicle toproceed, randomly generated portions of visual identifiers, audiosignals, or emitted signals may be used by the two vehicles to morequickly resolve such a stalemate or conflict by randomly assigningpriority to one of the vehicles. Such randomly generated portions ofvisual identifiers, audio signals, or emitted signals may also be usedto more quickly resolve potential conflicts that may occur with othercombinations of strategy modes and/or actions.

FIG. 10 is a flow diagram illustrating an example strategy modebroadcast process 1000 for autonomous vehicle operations, according toan implementation. The process may begin by selecting a strategy modefor the autonomous vehicle, as at 1002, as further described herein.Then, the process may determine whether additional information, inaddition to the selected strategy mode, is to be broadcast, as at 1004.For example, the additional information may include a time of broadcast,a position of the vehicle, a speed or velocity of the vehicle, apriority of the vehicle, a state of the vehicle, capabilities of thevehicle, an identity of the vehicle, a vehicle type of the vehicle, orany other information. If additional information is to be broadcasttogether with the selected strategy mode, then the process proceeds todetermine the additional information to be broadcast, as at 1006.

If no additional information is to be broadcast with the selectedstrategy mode, or after determining the additional information to bebroadcast, the process may proceed to encode and broadcast the selectedstrategy mode, and any additional information, as at 1008. The selectedstrategy mode, and any additional information, may be broadcast usingone or more notification devices as described herein. For example, thevisual notification devices may be used, as at 1010, audio notificationdevices may be used, as at 1012, or signal notification devices may beused, as at 1014. Alternatively or in addition, the selected strategymode, and any additional information, may be broadcast using multipletypes of notification devices, e.g., two or more different types ofvisual, audio, or signal notification devices, and/or two or more ofvisual, audio, and signal notification devices. The process then ends,as at 1016.

The process 1000 may repeat continuously or intermittently. For example,the autonomous vehicle may select a new strategy mode based on monitoredchanges to other vehicles, the environment, or its own operations orcapabilities. Then, the autonomous vehicle may determine whether anyadditional or updated information should be broadcast together with thenew, selected strategy mode. The autonomous vehicle may then determineto broadcast the new, selected strategy mode and any additionalinformation using any one or more of the notification devices of thevehicle.

Referring again to FIG. 9, the autonomous vehicle 900 may also includevarious sensing devices 930 to detect a broadcast strategy mode, orother broadcast information. For example, the sensing devices 930 mayinclude various types of sensing devices, such as visual sensingdevices, audio sensing devices, or signal receiving devices to detect abroadcast strategy mode, or other broadcast information. For example,the autonomous vehicle 900 may include various sensing devices 930-1,930-2, 930-3, 930-4, 930-5, 930-6, 930-7 arranged around the vehicle900. While FIG. 9 shows a particular number and arrangement of sensingdevices 930, the autonomous vehicle 900 may include any other number orarrangement of sensing devices 930.

Visual sensing devices 930 may include imaging devices, imaging sensors,radar, LIDAR, time of flight sensors, thermal or infrared sensors, orother visual sensing devices to detect broadcast visual identifiers,license plates, displays, colors, symbols, or other identifiers on oneor more surfaces or locations of a sensed vehicle. The visual sensingdevices may detect, decode, and/or decipher information related to abroadcast strategy mode, or other broadcast information. In addition,the visual sensing devices 930 may also detect, decode, and/or decipherthe illumination or flashing of one or more lights 920 of a sensedvehicle. Further, the visual sensing devices 930 may also detect,decode, and/or decipher defined motions performed by a sensed vehicle.The visual identifiers detected by the visual sensing devices 930 may ormay not be visible to humans, and/or may or may not be discernible ordecipherable by humans.

Audio sensing devices 930 may include microphones or other sound ornoise-receiving devices at one or more locations of the vehicle. Theaudio sensing devices may detect, decode, and/or decipher sounds ornoises related to a broadcast strategy mode, or other broadcastinformation. The sounds or noises detected by the audio sensing devicesmay or may not be audible to humans, and/or may or may not bediscernible or decipherable by humans.

Signal receiving devices 930 may include signal receivers,radiofrequency identification readers, or other electromagnetic signalreceivers at one or more locations of the vehicle. The signal receivingdevices may detect, decode, and/or decipher signals related to abroadcast strategy mode, or other broadcast information. The signalsdetected by the signal receiving devices may or may not be detectable byhumans, and/or may or may not be discernible or decipherable by humans.

In addition to the selected strategy mode, the autonomous vehicle 900may detect various other types of information, such as a time ofbroadcast, a position of the sensed vehicle, a speed or velocity of thesensed vehicle, a priority of the sensed vehicle, a state of the sensedvehicle, capabilities of the sensed vehicle, an identity of the sensedvehicle, a vehicle type of the sensed vehicle, or any other information.

Further, the autonomous vehicle 900 may detect actions of other vehiclesusing any of the sensing devices, and the autonomous vehicle 900 maypredict strategy modes of the other vehicles based on the detectedactions. For example, if an autonomous vehicle detects a second vehicletaking an uncoupled action, e.g., making a U-turn at an intersection andtraveling away from the autonomous vehicle, the autonomous vehicle maypredict that the second vehicle is operating in an uncoupled strategymode. In addition, if an autonomous vehicle detects a second vehiclecontinuing its current actions without change, e.g., continuing to moveat a slow speed through an intersection past the autonomous vehicle, theautonomous vehicle may predict that the second vehicle is operating in apermissive strategy mode. Further, if an autonomous vehicle detects asecond vehicle changing its operations in order to facilitate actions ofthe autonomous vehicle, e.g., slowing down in response to a lane changeoperation of the autonomous vehicle, the autonomous vehicle may predictthat the second vehicle is operating in an assistive strategy mode.Moreover, if an autonomous vehicle detects a second vehicle changing itsoperations in order to prevent actions of the autonomous vehicle, e.g.,blocking a forward travel of the autonomous vehicle, the autonomousvehicle may predict that the second vehicle is operating in apreventative strategy mode.

In addition to the various sensing devices 930 on the vehicle, theautonomous vehicle may also include other types of sensing devices. Forexample, the autonomous vehicle may include environmental sensors todetect aspects of the environment, e.g., imaging devices, imagingsensors, radar, LIDAR, time of flight sensors, thermal or infraredsensors, microphones or other sound or noise-receiving devices,temperature sensors, humidity sensors, pressure sensors, accelerationsensors, elevation sensors, inclination sensors, and other types ofsensors. Further, the autonomous vehicle may include other onboardsensors to detect aspects of the vehicle function or operation, e.g.,fuel sensors, battery sensors, temperature sensors, tire pressuresensors, weight sensors, acceleration sensors, turning radius sensors,or other onboard sensors to detect functions or capabilities of thevehicle.

Through the use of such environmental and/or onboard sensors, theautonomous vehicle may detect aspects of the environment, such as roadsigns, traffic signals, pedestrians, cyclists, animals, trees,buildings, obstructions, natural or artificial obstacles, human-operatedvehicles, disabled or malfunctioning vehicles, dynamic environmentalconditions such as wind/gusts, rain, snow, ice, or pressure, or otherelements in the environment. In addition, through the use of suchenvironmental and/or onboard sensors, the autonomous vehicle may detector determine topological constraints associated with the environment,such as roadway or pathway characteristics or conditions, weatherassociated the environment, or characteristics or capabilities ofvehicles.

In an alternative embodiment, one or more of the notification devices920, 922 and/or one or more of the sensing devices 930, along with anyassociated processor(s), memor(ies), controller(s), and/or input/outputdevices, may be provided in a separate system that may be installed orused in a vehicle, e.g., an autonomous vehicle, or a non-autonomous,human-operated vehicle. For example, the separate system may includenotification devices 920, 922 that may broadcast a strategy mode of thevehicle, which may be permanently encoded and associated with theseparate system, or which may be selected by a controller of theseparate system based on detected aspects of other vehicles and/or theenvironment, as described herein, or which may be selected by a humanoperator via interaction with one or more input devices, e.g., buttons,displays, menus, visual interfaces, audio interfaces, etc., associatedwith and in communication with the separate system. In addition, thenotification devices 920, 922 may also broadcast other informationassociated with the vehicle. As described herein, the notificationdevices 920, 922 may be any one or a combination of visual, audio, orsignal notification devices.

Further, the separate system may include sensing devices 930 that maydetect aspects of other vehicles to determine strategy modes and/oractions of the other vehicles, and/or that may detect aspects of theenvironment to determine elements of the environment and/or topologicalconstraints associated with the environment. Moreover, the sensingdevices 930 of the separate system may also detect aspects of thevehicle characteristics and/or capabilities, as described herein.

For particular embodiments in which the separate system is to be used ina non-autonomous, human-operated vehicle, the separate system mayinclude input/output devices to enable interaction with the separatesystem by a human operator. For example, the separate system may includeinput devices, e.g., buttons, displays, menus, visual interfaces, audiointerfaces, etc., that may enable a human operator to perform variousfunctions with the separate system, such as power on/power off theseparate system, select an operational goal, select a strategy mode,broadcast a strategy mode, select other information to broadcast, selecta notification device for broadcast, select an action based on theselected strategy mode, select a sensing device for detection of aspectsof other vehicles and/or the environment, or other functions. Inaddition, the separate system may include output devices, e.g.,displays, menus, visual interfaces, audio interfaces, etc., that mayenable the separate system to provide information related to strategymodes, actions, elements in the environment, topological constraints,and/or operational goals, suggestions, recommendations, warnings,feedback, or other information to a human operator based on detectedaspects of other vehicles and/or the environment. Accordingly, theseparate system may be used in non-autonomous, human-operated vehicles,and/or in various different types of autonomous vehicles that may not beable communicate directly with each other, to enable any such vehiclesto safely and resolvably interoperate with each other using the strategymodes, actions, and operational goals as described herein.

Moreover, such a separate system with one or more of the notificationdevices 920, 922 and/or one or more of the sensing devices 930, alongwith any associated processor(s), memor(ies), controller(s), and/orinput/output devices, may also be used by cyclists, pedestrians, or anyother entities or humans that desire to broadcast and/or sense strategymodes, actions, aspects of other vehicles, aspects of the environment,and/or other information in order to safely and resolvably interoperatewith other autonomous and/or non-autonomous vehicles or systems.

FIG. 11 is a flow diagram illustrating an example strategy modedetermination process 1100 for autonomous vehicle operations, accordingto an implementation. The process may begin by detecting aspects ofother vehicles, as at 1102. For example, broadcast information relatedto strategy modes and/or other information of other vehicles in a localenvironment near the autonomous vehicle may be detected using one ormore sensing devices, as described herein. The process may continue bydetecting aspects of the environment, as at 1104. For example, aspectsof the local environment near the autonomous vehicle may be detectedusing one or more sensing devices, as described herein. The process maythen proceed to determining strategy modes of other vehicles based atleast in part on the detected aspects of the other vehicles, as at 1106.For example, the control system of the autonomous vehicle may processthe detected broadcast information, e.g., received visual identifiers,audio identifiers, or signals, to determine strategy modes of the othervehicles.

The process may continue by determining elements in the environmentbased at least in part on the detected aspects of the environment, as at1108. For example, the control system of the autonomous vehicle mayprocess the detected aspects of the environment to identify one or moreelements in the environment, e.g., road signs, traffic signals,pedestrians, cyclists, animals, trees, buildings, obstructions, naturalor artificial obstacles, human-operated vehicles, disabled ormalfunctioning vehicles, dynamic environmental conditions such aswind/gusts, rain, snow, ice, or pressure, or other elements in theenvironment. Then, the process may determine topological constraintsassociated with the environment, as at 1110. For example, thetopological constraints may be determined based at least in part on thedetected aspects of the environment and the determined elements in theenvironment.

The process may then continue to determine or receive operational goalsof the autonomous vehicle, as at 1112. For example, the operationalgoals may include one or more of safety, resolvability, efficiency,time, priority, throughput, on-time completion, average speed, number ofincidents or conflicts, fuel or resource utilization, and/or othergoals.

Then, the process may proceed to selecting a strategy mode for theautonomous vehicle based at least in part on the determined strategymodes of other vehicles, the determined elements in the environment, thedetermined topological constraints, and the determined operationalgoals, as at 1114. For example, the autonomous vehicle may select astrategy mode from among an uncoupled strategy mode, a permissivestrategy mode, an assistive strategy mode, and a preventative strategymode. Then, the process may continue by selecting one or more actionsfor the autonomous vehicle based at least in part on the selectedstrategy mode for the autonomous vehicle, as at 1116. For example, theset of available actions for the autonomous vehicle may be determinedbased at least in part on the selected strategy mode. In addition, theset of available actions may be further determined or filtered based onthe determined elements in the environment and/or the topologicalconstraints associated with the environment. The process may then end,as at 1118.

The process 1100 may repeat continuously or intermittently. For example,the autonomous vehicle may monitor for changes to the detected aspectsof other vehicles or the environment, changes to the determined strategymodes of other vehicles, changes to the determined elements in theenvironment and topological constraints associated with the environment,and/or changes to its operational goals. Based on any changes to theother vehicles, the environment, its own operations or capabilities, orthe operational goals, the autonomous vehicle may select a new strategymode and corresponding actions.

FIG. 12 is a block diagram illustrating various components of an exampleautonomous vehicle control system 950 for selection, broadcast, anddetermination of strategy modes for autonomous vehicle operations,according to an implementation. In various examples, the block diagrammay be illustrative of one or more aspects of the autonomous vehiclecontrol system 950 that may be used to implement the various systems andprocesses discussed above. In the illustrated implementation, theautonomous vehicle control system 950 includes one or more processors1202, coupled to a non-transitory computer readable storage medium 1220via an input/output (I/O) interface 1210. The autonomous vehicle controlsystem 950 may also include a propulsion mechanism 1204, a power supply1206, a steering mechanism 1207, and/or a navigation system 1208. Theautonomous vehicle control system 950 further includes one or morebroadcasting/notification devices 1212, one or more sensing devices1214, a strategy mode controller 1215, a network interface 1216, and oneor more input/output devices 1218.

In various implementations, the autonomous vehicle control system 950may be a uniprocessor system including one processor 1202, or amultiprocessor system including several processors 1202 (e.g., two,four, eight, or another suitable number). The processor(s) 1202 may beany suitable processor capable of executing instructions. For example,in various implementations, the processor(s) 1202 may be general-purposeor embedded processors implementing any of a variety of instruction setarchitectures (ISAs), such as the x86, PowerPC, SPARC, or MIPS ISAs, orany other suitable ISA. In multiprocessor systems, each processor(s)1202 may commonly, but not necessarily, implement the same ISA.

The non-transitory computer readable storage medium 1220 may beconfigured to store executable instructions, data, strategy modes,actions, elements of environments, topological constraints associatedwith environments, operational goals, and/or data items accessible bythe processor(s) 1202. In various implementations, the non-transitorycomputer readable storage medium 1220 may be implemented using anysuitable memory technology, such as static random access memory (SRAM),synchronous dynamic RAM (SDRAM), nonvolatile/Flash-type memory, or anyother type of memory. In the illustrated implementation, programinstructions and data implementing desired functions, such as thosedescribed above, are shown stored within the non-transitory computerreadable storage medium 1220 as program instructions 1222, data storage1224 and strategy modes, actions, environment, topological constraints,and operational goals 1226, respectively. In other implementations,program instructions, data and/or strategy modes, actions, environment,topological constraints, and operational goals may be received, sent orstored upon different types of computer-accessible media, such asnon-transitory media, or on similar media separate from thenon-transitory computer readable storage medium 1220 or the autonomousvehicle control system 950.

Generally speaking, a non-transitory, computer readable storage mediummay include storage media or memory media such as magnetic or opticalmedia, e.g., disk or CD/DVD-ROM, coupled to the autonomous vehiclecontrol system 950 via the I/O interface 1210. Program instructions anddata stored via a non-transitory computer readable medium may betransmitted by transmission media or signals, such as electrical,electromagnetic, or digital signals, which may be conveyed via acommunication medium such as a network and/or a wireless link, such asmay be implemented via the network interface 1216.

In one implementation, the I/O interface 1210 may be configured tocoordinate I/O traffic between the processor(s) 1202, the non-transitorycomputer readable storage medium 1220, and any peripheral devices, thenetwork interface 1216 or other peripheral interfaces, such asinput/output devices 1218. In some implementations, the I/O interface1210 may perform any necessary protocol, timing or other datatransformations to convert data signals from one component (e.g.,non-transitory computer readable storage medium 1220) into a formatsuitable for use by another component (e.g., processor(s) 1202). In someimplementations, the I/O interface 1210 may include support for devicesattached through various types of peripheral buses, such as a variant ofthe Peripheral Component Interconnect (PCI) bus standard or theUniversal Serial Bus (USB) standard, for example. In someimplementations, the function of the I/O interface 1210 may be splitinto two or more separate components, such as a north bridge and a southbridge, for example. Also, in some implementations, some or all of thefunctionality of the I/O interface 1210, such as an interface to thenon-transitory computer readable storage medium 1220, may beincorporated directly into the processor(s) 1202.

The propulsion mechanism 1204 and the steering mechanism 1207 maycommunicate with the navigation system 1208 and receive power from thepower supply 1206 to control the operation, routing, and navigation ofthe autonomous vehicle. The navigation system 1208 may include a GPS orother similar system than can be used to navigate the autonomous vehicleto and/or from a location.

The autonomous vehicle control system 950 may also include one or morebroadcasting/notification devices 1212 that may be used to broadcastinformation associated with the vehicle, including strategy modes,actions, aspects of the environments or other information. Theautonomous vehicle control system 950 may also include one or moresensing devices 1214 that may be used to detect information broadcast byother vehicles, including strategy modes, actions, aspects of theenvironments or other information.

The autonomous vehicle control system 950 may also include a strategymode controller 1215. The strategy mode controller 1215 may communicatewith the broadcasting/notification devices 1212, the sensing devices1214, the processor(s) 1202, the memory 1220, the propulsion mechanism1204, the steering mechanism 1207, and the navigation system 1208. Thestrategy mode controller 1215 may receive information detected by thesensing devices 1214, and in combination with data stored in the memory1220, select strategy modes and/or actions for the autonomous vehicle.The strategy mode controller 1215 may also broadcast information, incombination with data stored in the memory 1220, using thebroadcasting/notification devices 1212. The strategy mode controller1215 may also communicate with the propulsion mechanism 1204, thesteering mechanism 1207, and the navigation system 1208 to control theoperation, routing, and navigation of the autonomous vehicle based onselected strategy modes and/or actions for the autonomous vehicle.

The network interface 1216 may be configured to allow data to beexchanged between the autonomous vehicle control system 950, otherdevices attached to a network, such as other computer systems, theautonomous vehicle management system 640, 740 described with respect toFIG. 13, autonomous vehicle control systems of other autonomousvehicles, and/or separate systems installed or used in other autonomousor non-autonomous vehicles or by other entities or humans, e.g.,cyclists, pedestrians. For example, the network interface 1216 mayenable wireless communication between numerous autonomous vehicles. Invarious implementations, the network interface 1216 may supportcommunication via wireless general data networks, such as a Wi-Finetwork. For example, the network interface 1216 may supportcommunication via telecommunications networks such as cellularcommunication networks, satellite networks, and the like.

Input/output devices 1218 may, in some implementations, include one ormore displays, buttons, keyboards, keypads, touchpads, mice,touchscreens, projection devices, visual interfaces, audio outputdevices, audio interfaces, voice or optical recognition devices, imagecapture devices, scanning devices, thermal sensors, infrared sensors,time of flight sensors, accelerometers, pressure sensors, weathersensors, other types of sensors described herein, other types ofinput/output devices described herein, etc. Multiple input/outputdevices 1218 may be present and controlled by the autonomous vehiclecontrol system 950.

As shown in FIG. 12, the memory may include program instructions 1222which may be configured to implement the example processes and/orsub-processes described above. The data storage 1224 may include variousdata stores for maintaining data items that may be provided fordetermining or selecting strategy modes, actions, aspects or elements ofthe environments, topological constraints associated with theenvironments, or operational goals, broadcasting strategy modes andadditional information, detecting strategy modes and additionalinformation, etc. The strategy modes may include an uncoupled strategymode, a permissive strategy mode, an assistive strategy mode, and apreventative strategy mode. The actions may include any sets ofavailable actions associated with various strategy modes, elements inthe environment, or topological constraints. The operational goals mayinclude any metrics or goals for which operations of one or moreautonomous vehicles may be coordinated and/or optimized.

FIG. 13 is a block diagram illustrating various components of an exampleautonomous vehicle management system 640, 740 for coordinatedoptimization of strategy modes for autonomous vehicle operations,according to an implementation.

Various operations of an autonomous vehicle management system, such asthose described herein, may be executed on one or more computer systems,interacting with various other devices, according to variousimplementations. In the illustrated implementation, the autonomousvehicle management system 640, 740 includes one or more processors1310A, 1310B through 1310N, coupled to a non-transitorycomputer-readable storage medium 1320 via an input/output (I/O)interface 1330. The autonomous vehicle management system 640, 740further includes a network interface 1340 coupled to the I/O interface1330, and one or more input/output devices 1350. In someimplementations, it is contemplated that the autonomous vehiclemanagement system may be implemented using a single instance of theautonomous vehicle management system 640, 740, while in otherimplementations, multiple such systems or multiple nodes making up theautonomous vehicle management system 640, 740 may be configured to hostdifferent portions or instances of the autonomous vehicle managementsystem. For example, in one implementation, some data sources orservices may be implemented via one or more nodes of the autonomousvehicle management system 640, 740 that are distinct from those nodesimplementing other data sources or services. In some implementations, agiven node may implement the functionality of more than one component ofthe autonomous vehicle management system.

In various implementations, the autonomous vehicle management system640, 740 may be a uniprocessor system including one processor 1310A, ora multiprocessor system including several processors 1310A-1310N (e.g.,two, four, eight, or another suitable number). The processors1310A-1310N may be any suitable processor capable of executinginstructions. For example, in various implementations, the processors1310A-1310N may be general-purpose or embedded processors implementingany of a variety of instruction set architectures (ISAs), such as thex86, PowerPC, SPARC, or MIPS ISAs, or any other suitable ISA. Inmultiprocessor systems, each of the processors 1310A-1310N may commonly,but not necessarily, implement the same ISA.

The non-transitory computer-readable storage medium 1320 may beconfigured to store executable instructions and/or data accessible bythe one or more processors 1310A-1310N. In various implementations, thenon-transitory computer-readable storage medium 1320 may be implementedusing any suitable memory technology, such as static random accessmemory (SRAM), synchronous dynamic RAM (SDRAM), nonvolatile/Flash-typememory, or any other type of memory. In the illustrated implementation,program instructions and data implementing desired functions, such asthose described above, are shown stored within the non-transitorycomputer-readable storage medium 1320 as program instructions 1322, datastorage 1324, and strategy modes, actions, environment, topologicalconstraints, and operational goals 1326, respectively. In otherimplementations, program instructions and/or data may be received, sentor stored upon different types of computer-accessible media, such asnon-transitory media, or on similar media separate from thenon-transitory computer-readable storage medium 1320 or the autonomousvehicle management system 640, 740. Generally speaking, anon-transitory, computer-readable storage medium may include storagemedia or memory media such as magnetic or optical media, e.g., disk orCD/DVD-ROM coupled to the autonomous vehicle management system 640, 740via the I/O interface 1330. Program instructions and data stored via anon-transitory computer-readable medium may be transmitted bytransmission media or signals, such as electrical, electromagnetic, ordigital signals, which may be conveyed via a communication medium, suchas a network and/or a wireless link, such as may be implemented via thenetwork interface 1340.

In one implementation, the I/O interface 1330 may be configured tocoordinate I/O traffic between the processors 1310A-1310N, thenon-transitory computer-readable storage medium 1320, and any peripheraldevices in the device, including the network interface 1340 or otherperipheral interfaces, such as input/output devices 1350. In someimplementations, the I/O interface 1330 may perform any necessaryprotocol, timing or other data transformations to convert data signalsfrom one component (e.g., non-transitory computer-readable storagemedium 1320) into a format suitable for use by another component (e.g.,processors 1310A-1310N). In some implementations, the I/O interface 1330may include support for devices attached through various types ofperipheral buses, such as a variant of the Peripheral ComponentInterconnect (PCI) bus standard or the Universal Serial Bus (USB)standard, for example. In some implementations, the function of the I/Ointerface 1330 may be split into two or more separate components, suchas a north bridge and a south bridge, for example. Also, in someimplementations, some or all of the functionality of the I/O interface1330, such as an interface to the non-transitory computer-readablestorage medium 1320, may be incorporated directly into the processors1310A-1310N.

The autonomous vehicle management system 640, 740 may include a systemoperations controller 1332. The system operations controller 1332 maycommunicate with a plurality of autonomous vehicles in order to receiveand/or store data or information associated with strategy modes,actions, aspects or elements in the environments, topologicalconstraints associated with the environments, and/or operational goals.The system operations controller 1332 may then process the received orstored information based on one or more operational goals of theautonomous vehicle management system 640, 740. The system operationscontroller 1332 may then communicate with one or more of the pluralityof autonomous vehicles to instruct modifications to their operations toachieve the operational goals of the autonomous vehicle managementsystem 640, 740.

The network interface 1340 may be configured to allow data to beexchanged between the autonomous vehicle management system 640, 740 andother devices attached to a network, such as other computer systems, theautonomous vehicle control systems 950 of autonomous vehicles describedwith respect to FIG. 12, and/or separate systems installed or used inother autonomous or non-autonomous vehicles or by other entities orhumans, e.g., cyclists, pedestrians. In various implementations, thenetwork interface 1340 may support communication via wired or wirelessgeneral data networks, such as any suitable type of Ethernet network.For example, the network interface 1340 may support communication viatelecommunications/telephony networks such as analog voice networks ordigital fiber communications networks, via storage area networks such asFibre Channel SANs, or via any other suitable type of network and/orprotocol.

Input/output devices 1350 may, in some implementations, include one ormore displays, buttons, keyboards, keypads, touchpads, mice,touchscreens, projection devices, visual interfaces, audio outputdevices, audio interfaces, voice or optical recognition devices, imagecapture devices, scanning devices, thermal sensors, infrared sensors,time of flight sensors, accelerometers, pressure sensors, weathersensors, other types of sensors described herein, other types ofinput/output devices described herein, or any other devices suitable forentering or retrieving data by the autonomous vehicle management system640, 740. Multiple input/output devices 1350 may be present in theautonomous vehicle management system 640, 740 or may be distributed onvarious nodes of the autonomous vehicle management system 640, 740. Insome implementations, similar input/output devices may be separate fromthe autonomous vehicle management system 640, 740 and may interact withone or more nodes of the autonomous vehicle management system 640, 740through a wired or wireless connection, such as over the networkinterface 1340.

As shown in FIG. 13, the computer-readable storage medium 1320 mayinclude program instructions 1322 which may be configured to implementan autonomous vehicle management system, data storage 1324 which maycomprise various tables, databases and/or other data structuresaccessible by the program instructions 1322, and data related tostrategy modes, actions, environment, topological constraints, andoperational goals 1326. In one implementation, the program instructions1322 may include various software modules configured to implement andcoordinate operations of the various components of the autonomousvehicle management system 640, 740. The data storage 1324 and other data1326 may include various data stores for maintaining control andcoordination between the various components of the autonomous vehiclemanagement system 640, 740, such as data representing strategy modes oractions of a plurality of vehicles, aspects or elements of variousenvironments, topological constraints associated with variousenvironments, and various operational goals for the autonomous vehiclemanagement system.

In various implementations, the parameter values and other dataillustrated herein as being included in one or more data stores may becombined with other information not described or may be partitioneddifferently into more, fewer, or different data structures. In someimplementations, data stores may be physically located in one memory ormay be distributed among two or more memories.

Each process described herein may be implemented by the architecturesdescribed herein or by other architectures. The processes areillustrated as a collection of blocks in a logical flow. Some of theblocks represent operations that can be implemented in hardware,software, or a combination thereof. In the context of software, theblocks represent computer-executable instructions stored on one or morecomputer readable media that, when executed by one or more processors,perform the recited operations. Generally, computer-executableinstructions include routines, programs, objects, components, datastructures, and the like that perform particular functions or implementparticular abstract data types.

The computer readable media may include non-transitory computer readablestorage media, which may include hard drives, floppy diskettes, opticaldisks, CD-ROMs, DVDs, read-only memories (ROMs), random access memories(RAMs), EPROMs, EEPROMs, flash memory, magnetic or optical cards,solid-state memory devices, or other types of storage media suitable forstoring electronic instructions. In addition, in some implementations,the computer readable media may include a transitory computer readablesignal (in compressed or uncompressed form). Examples of computerreadable signals, whether modulated using a carrier or not, include, butare not limited to, signals that a computer system hosting or running acomputer program can be configured to access, including signalsdownloaded through the Internet or other networks. Finally, the order inwhich the operations are described is not intended to be construed as alimitation, and any number of the described operations can be combinedin any order and/or in parallel to implement the process. Additionally,one or more of the operations may be considered optional and/or notutilized with other operations.

Those skilled in the art will appreciate that the autonomous vehiclecontrol system 950 and the autonomous vehicle management system 640, 740are merely illustrative and are not intended to limit the scope of thepresent disclosure. In particular, the computing system and devices mayinclude any combination of hardware or software that can perform theindicated functions, including computers, network devices, internetappliances, PDAs, wireless phones, pagers, etc. The autonomous vehiclecontrol system 950 and/or the autonomous vehicle management system 640,740 may also be connected to other devices that are not illustrated, orinstead may operate as a stand-alone system. In addition, thefunctionality provided by the illustrated components may, in someimplementations, be combined in fewer components or distributed inadditional components. Similarly, in some implementations, thefunctionality of some of the illustrated components may not be providedand/or other additional functionality may be available.

Those skilled in the art will also appreciate that, while various itemsare illustrated as being stored in memory or storage while being used,these items or portions of them may be transferred between memory andother storage devices for purposes of memory management and dataintegrity. Alternatively, in other implementations, some or all of thesoftware components may execute in memory on another device andcommunicate with the illustrated autonomous vehicle control system 950and/or the autonomous vehicle management system 640, 740. Some or all ofthe system components or data structures may also be stored (e.g., asinstructions or structured data) on a non-transitory,computer-accessible medium or a portable article to be read by anappropriate drive, various examples of which are described above. Insome implementations, instructions stored on a computer-accessiblemedium separate from the autonomous vehicle control system 950 and/orthe autonomous vehicle management system 640, 740 may be transmitted tothe autonomous vehicle control system 950 and/or the autonomous vehiclemanagement system 640, 740 via transmission media or signals, such aselectrical, electromagnetic, or digital signals, conveyed via acommunication medium, such as a network and/or a wireless link. Variousimplementations may further include receiving, sending or storinginstructions and/or data implemented in accordance with the foregoingdescription upon a computer-accessible medium. Accordingly, thetechniques described herein may be practiced with other autonomousvehicle control system configurations and/or other autonomous vehiclemanagement system configurations.

Those skilled in the art will appreciate that, in some implementations,the functionality provided by the processes and systems discussed abovemay be provided in alternative ways, such as being split among moresoftware modules or routines or consolidated into fewer modules orroutines. Similarly, in some implementations, illustrated processes andsystems may provide more or less functionality than is described, suchas when other illustrated processes instead lack or include suchfunctionality respectively, or when the amount of functionality that isprovided is altered. In addition, while various operations may beillustrated as being performed in a particular manner (e.g., in serialor in parallel) and/or in a particular order, those skilled in the artwill appreciate that, in other implementations, the operations may beperformed in other orders and in other manners. Those skilled in the artwill also appreciate that the data structures discussed above may bestructured in different manners, such as by having a single datastructure split into multiple data structures or by having multiple datastructures consolidated into a single data structure. Similarly, in someimplementations, illustrated data structures may store more or lessinformation than is described, such as when other illustrated datastructures instead lack or include such information respectively, orwhen the amount or types of information that is stored is altered. Thevarious processes and systems as illustrated in the figures anddescribed herein represent example implementations. The processes andsystems may be implemented in software, hardware, or a combinationthereof in other implementations. Similarly, the order of any processmay be changed and various elements may be added, reordered, combined,omitted, modified, etc., in other implementations.

From the foregoing, it will be appreciated that, although specificimplementations have been described herein for purposes of illustration,various modifications may be made without deviating from the spirit andscope of the appended claims and the features recited therein. Inaddition, while certain aspects are presented below in certain claimforms, the inventors contemplate the various aspects in any availableclaim form. For example, while only some aspects may currently berecited as being embodied in a computer readable storage medium, otheraspects may likewise be so embodied. Various modifications and changesmay be made as would be obvious to a person skilled in the art havingthe benefit of this disclosure. It is intended to embrace all suchmodifications and changes and, accordingly, the above description is tobe regarded in an illustrative rather than a restrictive sense.

What is claimed is:
 1. An autonomous ground-based vehicle, comprising: apropulsion mechanism; a steering mechanism; at least one imaging device;and a control system comprising a processor and a memory, the controlsystem configured to: capture, using the at least one imaging device, atleast one image of a second autonomous vehicle and an environment;determine a strategy mode of the second autonomous vehicle, at least oneaspect related to the environment, and at least one topologicalconstraint associated with the environment based at least in part on thecaptured at least one image, wherein at least one aspect associated withthe second autonomous vehicle within the at least one image indicatesthe strategy mode selected by the second autonomous vehicle; and selectan autonomous strategy mode from a plurality of autonomous strategymodes for the autonomous ground-based vehicle based at least in part onthe determined strategy mode of the second autonomous vehicle, thedetermined at least one aspect related to the environment, and thedetermined at least one topological constraint associated with theenvironment, wherein the autonomous strategy mode for the autonomousground-based vehicle comprises one of an uncoupled strategy mode, apermissive strategy mode, an assistive strategy mode, or a preventativestrategy mode; wherein each of the plurality of autonomous strategymodes comprises a respective set of available actions of the autonomousground-based vehicle.
 2. The autonomous ground-based vehicle of claim 1,wherein the strategy mode of the second autonomous vehicle is determinedbased at least in part on illumination or flashing of one or more lightsof the second autonomous vehicle.
 3. The autonomous ground-based vehicleof claim 1, wherein the strategy mode of the second autonomous vehicleis determined based at least in part on a visual identifier on a portionof the second autonomous vehicle.
 4. The autonomous ground-based vehicleof claim 1, wherein the strategy mode of the second autonomous vehicleis determined based at least in part on a motion of the secondautonomous vehicle.
 5. An autonomous vehicle, comprising: a propulsionmechanism; a steering mechanism; at least one sensor; and a controlsystem comprising a processor and a memory, the control systemconfigured to: detect, using the at least one sensor, at least oneaspect of a second autonomous vehicle; determine a strategy mode of thesecond autonomous vehicle based at least in part on the detected atleast one aspect, wherein the at least one aspect of the secondautonomous vehicle indicates the strategy mode selected by the secondautonomous vehicle; and select an autonomous strategy mode from aplurality of autonomous strategy modes for the autonomous vehicle basedat least in part on the determined strategy mode of the secondautonomous vehicle, wherein the autonomous strategy mode for theautonomous vehicle comprises one of an uncoupled strategy mode, apermissive strategy mode, an assistive strategy mode, or a preventativestrategy mode; wherein each of the plurality of autonomous strategymodes comprises a respective set of available actions of the autonomousvehicle.
 6. The autonomous vehicle of claim 5, wherein the at least onesensor comprises an imaging sensor that captures images of at least aportion of the second autonomous vehicle.
 7. The autonomous vehicle ofclaim 6, wherein the at least one aspect comprises at least one ofillumination or flashing of one or more lights of the second autonomousvehicle; and wherein the strategy mode of the second autonomous vehicleis determined based at least in part on the detected at least one ofillumination or flashing of the one or more lights.
 8. The autonomousvehicle of claim 6, wherein the at least one aspect comprises a visualidentifier on the at least a portion of the second autonomous vehicle,the visual identifier comprising at least one of a license plate, analphanumeric identifier, a symbol, or a color of the at least a portionof the second autonomous vehicle; and wherein the strategy mode of thesecond autonomous vehicle is determined based at least in part on thedetected visual identifier.
 9. The autonomous vehicle of claim 6,wherein the at least one aspect comprises a motion of the secondautonomous vehicle; and wherein the strategy mode of the secondautonomous vehicle is determined based at least in part on the detectedmotion of the second autonomous vehicle.
 10. The autonomous vehicle ofclaim 5, wherein the at least one sensor comprises at least one of asignal receiver or a radiofrequency receiver to receive a signal; andwherein the at least one aspect comprises a signal broadcast by thesecond autonomous vehicle that indicates the strategy mode of the secondautonomous vehicle.
 11. The autonomous vehicle of claim 5, wherein theat least one aspect further indicates at least one of a time ofbroadcast of a signal, a position of the second autonomous vehicle, avelocity of the second autonomous vehicle, a priority associated withthe second autonomous vehicle, a state of the second autonomous vehicle,capabilities of the second autonomous vehicle, an identity of the secondautonomous vehicle, or a vehicle type of the second autonomous vehicle.12. The autonomous vehicle of claim 5, wherein the at least one sensorcomprises at least one of a radar sensor or a LIDAR sensor; and whereinthe at least one aspect comprises at least one of a position, avelocity, or a vehicle type of the second autonomous vehicle.
 13. Theautonomous vehicle of claim 5, wherein the control system is configuredto determine the strategy mode of the second autonomous vehicle bypredicting the strategy mode of the second autonomous vehicle based atleast in part on the detected at least one aspect of the secondautonomous vehicle.
 14. The autonomous vehicle of claim 5, wherein thecontrol system is further configured to: detect, using the at least onesensor, at least one aspect related to an environment; wherein the atleast one aspect related to the environment comprises an element in theenvironment, the element comprising at least one of a natural obstacle,a building, a weather formation, a pathway characteristic, a pedestrian,a cyclist, or an animal; wherein the autonomous strategy mode for theautonomous vehicle is further selected based at least in part on thedetected at least one aspect related to the environment.
 15. Theautonomous vehicle of claim 5, wherein the control system is furtherconfigured to: determine at least one topological constraint associatedwith an environment; wherein the at least one topological constraintcomprises at least one of pathway characteristics or conditions, weatherconditions associated the environment, or characteristics orcapabilities of the autonomous vehicle; wherein the autonomous strategymode for the autonomous vehicle is further selected based at least inpart on the determined at least one topological constraint associatedwith the environment.
 16. A method of operating an autonomous vehicle,comprising: detecting, using at least one sensor, at least one aspect ofa second autonomous vehicle; determining a strategy mode of the secondautonomous vehicle based at least in part on the detected at least oneaspect, wherein the at least one aspect of the second autonomous vehicleindicates the strategy mode selected by the second autonomous vehicle;selecting an autonomous strategy mode from a plurality of autonomousstrategy modes for the autonomous vehicle based at least in part on thedetermined strategy mode of the second autonomous vehicle, wherein theautonomous strategy mode for the autonomous vehicle comprises one of anuncoupled strategy mode, a permissive strategy mode, an assistivestrategy mode, or a preventative strategy mode; and selecting an actionfor the autonomous vehicle based at least in part on the selectedautonomous strategy mode for the autonomous vehicle, each of theplurality of autonomous strategy modes including a respective set ofavailable actions of the autonomous vehicle.
 17. The method of claim 16,further comprising: detecting, using the at least one sensor, at leastone aspect related to an environment; wherein the autonomous strategymode for the autonomous vehicle is further selected based at least inpart on the detected at least one aspect related to the environment. 18.The method of claim 16, further comprising: determining at least onetopological constraint associated with an environment; wherein theautonomous strategy mode for the autonomous vehicle is further selectedbased at least in part on the determined at least one topologicalconstraint associated with the environment.
 19. The method of claim 16,wherein determining the strategy mode of the second autonomous vehiclecomprises predicting the strategy mode of the second autonomous vehiclebased at least in part on the detected at least one aspect of the secondautonomous vehicle.